'''simple docstring'''




import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__UpperCAmelCase												={
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Optional[int]:
    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts    )
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config    )
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights    )

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Optional[Any]:
    if args.student_type == "roberta":
        __lowerCamelCase          =  False
    elif args.student_type == "gpt2":
        __lowerCamelCase          =  False






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Optional[int]:
    if args.student_type == "roberta":
        __lowerCamelCase          =  False






def __lowerCAmelCase			(							)     ->   Dict:
    __lowerCamelCase          =  argparse.ArgumentParser(description='''Training'''    )
    parser.add_argument('''--force'''    ,  action='''store_true'''    ,  help='''Overwrite dump_path if it already exists.'''    )

    parser.add_argument(
        '''--dump_path'''    ,  type=UpperCamelCase__    ,  required=UpperCamelCase__    ,  help='''The output directory (log, checkpoints, parameters, etc.)'''    )
    parser.add_argument(
        '''--data_file'''    ,  type=UpperCamelCase__    ,  required=UpperCamelCase__    ,  help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'''    ,  )

    parser.add_argument(
        '''--student_type'''    ,  type=UpperCamelCase__    ,  choices=['''distilbert''', '''roberta''', '''gpt2''']    ,  required=UpperCamelCase__    ,  help='''The student type (DistilBERT, RoBERTa).'''    ,  )
    parser.add_argument('''--student_config'''    ,  type=UpperCamelCase__    ,  required=UpperCamelCase__    ,  help='''Path to the student configuration.'''    )
    parser.add_argument(
        '''--student_pretrained_weights'''    ,  default=UpperCamelCase__    ,  type=UpperCamelCase__    ,  help='''Load student initialization checkpoint.'''    )

    parser.add_argument(
        '''--teacher_type'''    ,  choices=['''bert''', '''roberta''', '''gpt2''']    ,  required=UpperCamelCase__    ,  help='''Teacher type (BERT, RoBERTa).'''    )
    parser.add_argument('''--teacher_name'''    ,  type=UpperCamelCase__    ,  required=UpperCamelCase__    ,  help='''The teacher model.'''    )

    parser.add_argument('''--temperature'''    ,  default=2.0    ,  type=UpperCamelCase__    ,  help='''Temperature for the softmax temperature.'''    )
    parser.add_argument(
        '''--alpha_ce'''    ,  default=0.5    ,  type=UpperCamelCase__    ,  help='''Linear weight for the distillation loss. Must be >=0.'''    )
    parser.add_argument(
        '''--alpha_mlm'''    ,  default=0.0    ,  type=UpperCamelCase__    ,  help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'''    ,  )
    parser.add_argument('''--alpha_clm'''    ,  default=0.5    ,  type=UpperCamelCase__    ,  help='''Linear weight for the CLM loss. Must be >=0.'''    )
    parser.add_argument('''--alpha_mse'''    ,  default=0.0    ,  type=UpperCamelCase__    ,  help='''Linear weight of the MSE loss. Must be >=0.'''    )
    parser.add_argument(
        '''--alpha_cos'''    ,  default=0.0    ,  type=UpperCamelCase__    ,  help='''Linear weight of the cosine embedding loss. Must be >=0.'''    )

    parser.add_argument(
        '''--mlm'''    ,  action='''store_true'''    ,  help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''    )
    parser.add_argument(
        '''--mlm_mask_prop'''    ,  default=0.1_5    ,  type=UpperCamelCase__    ,  help='''Proportion of tokens for which we need to make a prediction.'''    ,  )
    parser.add_argument('''--word_mask'''    ,  default=0.8    ,  type=UpperCamelCase__    ,  help='''Proportion of tokens to mask out.'''    )
    parser.add_argument('''--word_keep'''    ,  default=0.1    ,  type=UpperCamelCase__    ,  help='''Proportion of tokens to keep.'''    )
    parser.add_argument('''--word_rand'''    ,  default=0.1    ,  type=UpperCamelCase__    ,  help='''Proportion of tokens to randomly replace.'''    )
    parser.add_argument(
        '''--mlm_smoothing'''    ,  default=0.7    ,  type=UpperCamelCase__    ,  help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'''    ,  )
    parser.add_argument('''--token_counts'''    ,  type=UpperCamelCase__    ,  help='''The token counts in the data_file for MLM.'''    )

    parser.add_argument(
        '''--restrict_ce_to_mask'''    ,  action='''store_true'''    ,  help='''If true, compute the distillation loss only the [MLM] prediction distribution.'''    ,  )
    parser.add_argument(
        '''--freeze_pos_embs'''    ,  action='''store_true'''    ,  help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'''    ,  )
    parser.add_argument(
        '''--freeze_token_type_embds'''    ,  action='''store_true'''    ,  help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'''    ,  )

    parser.add_argument('''--n_epoch'''    ,  type=UpperCamelCase__    ,  default=3    ,  help='''Number of pass on the whole dataset.'''    )
    parser.add_argument('''--batch_size'''    ,  type=UpperCamelCase__    ,  default=5    ,  help='''Batch size (for each process).'''    )
    parser.add_argument(
        '''--group_by_size'''    ,  action='''store_false'''    ,  help='''If true, group sequences that have similar length into the same batch. Default is true.'''    ,  )

    parser.add_argument(
        '''--gradient_accumulation_steps'''    ,  type=UpperCamelCase__    ,  default=50    ,  help='''Gradient accumulation for larger training batches.'''    ,  )
    parser.add_argument('''--warmup_prop'''    ,  default=0.0_5    ,  type=UpperCamelCase__    ,  help='''Linear warmup proportion.'''    )
    parser.add_argument('''--weight_decay'''    ,  default=0.0    ,  type=UpperCamelCase__    ,  help='''Weight decay if we apply some.'''    )
    parser.add_argument('''--learning_rate'''    ,  default=5E-4    ,  type=UpperCamelCase__    ,  help='''The initial learning rate for Adam.'''    )
    parser.add_argument('''--adam_epsilon'''    ,  default=1E-6    ,  type=UpperCamelCase__    ,  help='''Epsilon for Adam optimizer.'''    )
    parser.add_argument('''--max_grad_norm'''    ,  default=5.0    ,  type=UpperCamelCase__    ,  help='''Max gradient norm.'''    )
    parser.add_argument('''--initializer_range'''    ,  default=0.0_2    ,  type=UpperCamelCase__    ,  help='''Random initialization range.'''    )

    parser.add_argument(
        '''--fp16'''    ,  action='''store_true'''    ,  help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''    ,  )
    parser.add_argument(
        '''--fp16_opt_level'''    ,  type=UpperCamelCase__    ,  default='''O1'''    ,  help=(
            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
            '''See details at https://nvidia.github.io/apex/amp.html'''
        )    ,  )
    parser.add_argument('''--n_gpu'''    ,  type=UpperCamelCase__    ,  default=1    ,  help='''Number of GPUs in the node.'''    )
    parser.add_argument('''--local_rank'''    ,  type=UpperCamelCase__    ,  default=-1    ,  help='''Distributed training - Local rank'''    )
    parser.add_argument('''--seed'''    ,  type=UpperCamelCase__    ,  default=56    ,  help='''Random seed'''    )

    parser.add_argument('''--log_interval'''    ,  type=UpperCamelCase__    ,  default=5_00    ,  help='''Tensorboard logging interval.'''    )
    parser.add_argument('''--checkpoint_interval'''    ,  type=UpperCamelCase__    ,  default=40_00    ,  help='''Checkpoint interval.'''    )
    __lowerCamelCase          =  parser.parse_args()
    sanity_checks(UpperCamelCase__    )

    # ARGS #
    init_gpu_params(UpperCamelCase__    )
    set_seed(UpperCamelCase__    )
    if args.is_master:
        if os.path.exists(args.dump_path    ):
            if not args.force:
                raise ValueError(
                    f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                    ''' itUse `--force` if you want to overwrite it'''    )
            else:
                shutil.rmtree(args.dump_path    )

        if not os.path.exists(args.dump_path    ):
            os.makedirs(args.dump_path    )
        logger.info(f"""Experiment will be dumped and logged in {args.dump_path}"""    )

        # SAVE PARAMS #
        logger.info(f"""Param: {args}"""    )
        with open(os.path.join(args.dump_path    ,  '''parameters.json'''    )    ,  '''w'''    ) as f:
            json.dump(vars(UpperCamelCase__    )    ,  UpperCamelCase__    ,  indent=4    )
        git_log(args.dump_path    )

    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  MODEL_CLASSES[args.student_type]
    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    __lowerCamelCase          =  teacher_tokenizer_class.from_pretrained(args.teacher_name    )
    __lowerCamelCase          =  {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        __lowerCamelCase          =  tokenizer.all_special_tokens.index(UpperCamelCase__    )
        __lowerCamelCase          =  tokenizer.all_special_ids[idx]
    logger.info(f"""Special tokens {special_tok_ids}"""    )
    __lowerCamelCase          =  special_tok_ids
    __lowerCamelCase          =  tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(f"""Loading data from {args.data_file}"""    )
    with open(args.data_file    ,  '''rb'''    ) as fp:
        __lowerCamelCase          =  pickle.load(UpperCamelCase__    )

    if args.mlm:
        logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)"""    )
        with open(args.token_counts    ,  '''rb'''    ) as fp:
            __lowerCamelCase          =  pickle.load(UpperCamelCase__    )

        __lowerCamelCase          =  np.maximum(UpperCamelCase__    ,  1    ) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            __lowerCamelCase          =  0.0  # do not predict special tokens
        __lowerCamelCase          =  torch.from_numpy(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  None

    __lowerCamelCase          =  LmSeqsDataset(params=UpperCamelCase__    ,  data=UpperCamelCase__    )
    logger.info('''Data loader created.'''    )

    # STUDENT #
    logger.info(f"""Loading student config from {args.student_config}"""    )
    __lowerCamelCase          =  student_config_class.from_pretrained(args.student_config    )
    __lowerCamelCase          =  True

    if args.student_pretrained_weights is not None:
        logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""    )
        __lowerCamelCase          =  student_model_class.from_pretrained(args.student_pretrained_weights    ,  config=UpperCamelCase__    )
    else:
        __lowerCamelCase          =  student_model_class(UpperCamelCase__    )

    if args.n_gpu > 0:
        student.to(f"""cuda:{args.local_rank}"""    )
    logger.info('''Student loaded.'''    )

    # TEACHER #
    __lowerCamelCase          =  teacher_model_class.from_pretrained(args.teacher_name    ,  output_hidden_states=UpperCamelCase__    )
    if args.n_gpu > 0:
        teacher.to(f"""cuda:{args.local_rank}"""    )
    logger.info(f"""Teacher loaded from {args.teacher_name}."""    )

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(UpperCamelCase__    ,  UpperCamelCase__    )
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(UpperCamelCase__    ,  UpperCamelCase__    )

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0    ) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    __lowerCamelCase          =  Distiller(
        params=UpperCamelCase__    ,  dataset=UpperCamelCase__    ,  token_probs=UpperCamelCase__    ,  student=UpperCamelCase__    ,  teacher=UpperCamelCase__    )
    distiller.train()
    logger.info('''Let\'s go get some drinks.'''    )


if __name__ == "__main__":
  main()

'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


__UpperCAmelCase												={
    "Acehnese Arabic": "ace_Arab",
    "Acehnese Latin": "ace_Latn",
    "Mesopotamian Arabic": "acm_Arab",
    "Ta'izzi-Adeni Arabic": "acq_Arab",
    "Tunisian Arabic": "aeb_Arab",
    "Afrikaans": "afr_Latn",
    "South Levantine Arabic": "ajp_Arab",
    "Akan": "aka_Latn",
    "Amharic": "amh_Ethi",
    "North Levantine Arabic": "apc_Arab",
    "Modern Standard Arabic": "arb_Arab",
    "Modern Standard Arabic Romanized": "arb_Latn",
    "Najdi Arabic": "ars_Arab",
    "Moroccan Arabic": "ary_Arab",
    "Egyptian Arabic": "arz_Arab",
    "Assamese": "asm_Beng",
    "Asturian": "ast_Latn",
    "Awadhi": "awa_Deva",
    "Central Aymara": "ayr_Latn",
    "South Azerbaijani": "azb_Arab",
    "North Azerbaijani": "azj_Latn",
    "Bashkir": "bak_Cyrl",
    "Bambara": "bam_Latn",
    "Balinese": "ban_Latn",
    "Belarusian": "bel_Cyrl",
    "Bemba": "bem_Latn",
    "Bengali": "ben_Beng",
    "Bhojpuri": "bho_Deva",
    "Banjar Arabic": "bjn_Arab",
    "Banjar Latin": "bjn_Latn",
    "Standard Tibetan": "bod_Tibt",
    "Bosnian": "bos_Latn",
    "Buginese": "bug_Latn",
    "Bulgarian": "bul_Cyrl",
    "Catalan": "cat_Latn",
    "Cebuano": "ceb_Latn",
    "Czech": "ces_Latn",
    "Chokwe": "cjk_Latn",
    "Central Kurdish": "ckb_Arab",
    "Crimean Tatar": "crh_Latn",
    "Welsh": "cym_Latn",
    "Danish": "dan_Latn",
    "German": "deu_Latn",
    "Southwestern Dinka": "dik_Latn",
    "Dyula": "dyu_Latn",
    "Dzongkha": "dzo_Tibt",
    "Greek": "ell_Grek",
    "English": "eng_Latn",
    "Esperanto": "epo_Latn",
    "Estonian": "est_Latn",
    "Basque": "eus_Latn",
    "Ewe": "ewe_Latn",
    "Faroese": "fao_Latn",
    "Fijian": "fij_Latn",
    "Finnish": "fin_Latn",
    "Fon": "fon_Latn",
    "French": "fra_Latn",
    "Friulian": "fur_Latn",
    "Nigerian Fulfulde": "fuv_Latn",
    "Scottish Gaelic": "gla_Latn",
    "Irish": "gle_Latn",
    "Galician": "glg_Latn",
    "Guarani": "grn_Latn",
    "Gujarati": "guj_Gujr",
    "Haitian Creole": "hat_Latn",
    "Hausa": "hau_Latn",
    "Hebrew": "heb_Hebr",
    "Hindi": "hin_Deva",
    "Chhattisgarhi": "hne_Deva",
    "Croatian": "hrv_Latn",
    "Hungarian": "hun_Latn",
    "Armenian": "hye_Armn",
    "Igbo": "ibo_Latn",
    "Ilocano": "ilo_Latn",
    "Indonesian": "ind_Latn",
    "Icelandic": "isl_Latn",
    "Italian": "ita_Latn",
    "Javanese": "jav_Latn",
    "Japanese": "jpn_Jpan",
    "Kabyle": "kab_Latn",
    "Jingpho": "kac_Latn",
    "Kamba": "kam_Latn",
    "Kannada": "kan_Knda",
    "Kashmiri Arabic": "kas_Arab",
    "Kashmiri Devanagari": "kas_Deva",
    "Georgian": "kat_Geor",
    "Central Kanuri Arabic": "knc_Arab",
    "Central Kanuri Latin": "knc_Latn",
    "Kazakh": "kaz_Cyrl",
    "Kabiyè": "kbp_Latn",
    "Kabuverdianu": "kea_Latn",
    "Khmer": "khm_Khmr",
    "Kikuyu": "kik_Latn",
    "Kinyarwanda": "kin_Latn",
    "Kyrgyz": "kir_Cyrl",
    "Kimbundu": "kmb_Latn",
    "Northern Kurdish": "kmr_Latn",
    "Kikongo": "kon_Latn",
    "Korean": "kor_Hang",
    "Lao": "lao_Laoo",
    "Ligurian": "lij_Latn",
    "Limburgish": "lim_Latn",
    "Lingala": "lin_Latn",
    "Lithuanian": "lit_Latn",
    "Lombard": "lmo_Latn",
    "Latgalian": "ltg_Latn",
    "Luxembourgish": "ltz_Latn",
    "Luba-Kasai": "lua_Latn",
    "Ganda": "lug_Latn",
    "Luo": "luo_Latn",
    "Mizo": "lus_Latn",
    "Standard Latvian": "lvs_Latn",
    "Magahi": "mag_Deva",
    "Maithili": "mai_Deva",
    "Malayalam": "mal_Mlym",
    "Marathi": "mar_Deva",
    "Minangkabau Arabic ": "min_Arab",
    "Minangkabau Latin": "min_Latn",
    "Macedonian": "mkd_Cyrl",
    "Plateau Malagasy": "plt_Latn",
    "Maltese": "mlt_Latn",
    "Meitei Bengali": "mni_Beng",
    "Halh Mongolian": "khk_Cyrl",
    "Mossi": "mos_Latn",
    "Maori": "mri_Latn",
    "Burmese": "mya_Mymr",
    "Dutch": "nld_Latn",
    "Norwegian Nynorsk": "nno_Latn",
    "Norwegian Bokmål": "nob_Latn",
    "Nepali": "npi_Deva",
    "Northern Sotho": "nso_Latn",
    "Nuer": "nus_Latn",
    "Nyanja": "nya_Latn",
    "Occitan": "oci_Latn",
    "West Central Oromo": "gaz_Latn",
    "Odia": "ory_Orya",
    "Pangasinan": "pag_Latn",
    "Eastern Panjabi": "pan_Guru",
    "Papiamento": "pap_Latn",
    "Western Persian": "pes_Arab",
    "Polish": "pol_Latn",
    "Portuguese": "por_Latn",
    "Dari": "prs_Arab",
    "Southern Pashto": "pbt_Arab",
    "Ayacucho Quechua": "quy_Latn",
    "Romanian": "ron_Latn",
    "Rundi": "run_Latn",
    "Russian": "rus_Cyrl",
    "Sango": "sag_Latn",
    "Sanskrit": "san_Deva",
    "Santali": "sat_Olck",
    "Sicilian": "scn_Latn",
    "Shan": "shn_Mymr",
    "Sinhala": "sin_Sinh",
    "Slovak": "slk_Latn",
    "Slovenian": "slv_Latn",
    "Samoan": "smo_Latn",
    "Shona": "sna_Latn",
    "Sindhi": "snd_Arab",
    "Somali": "som_Latn",
    "Southern Sotho": "sot_Latn",
    "Spanish": "spa_Latn",
    "Tosk Albanian": "als_Latn",
    "Sardinian": "srd_Latn",
    "Serbian": "srp_Cyrl",
    "Swati": "ssw_Latn",
    "Sundanese": "sun_Latn",
    "Swedish": "swe_Latn",
    "Swahili": "swh_Latn",
    "Silesian": "szl_Latn",
    "Tamil": "tam_Taml",
    "Tatar": "tat_Cyrl",
    "Telugu": "tel_Telu",
    "Tajik": "tgk_Cyrl",
    "Tagalog": "tgl_Latn",
    "Thai": "tha_Thai",
    "Tigrinya": "tir_Ethi",
    "Tamasheq Latin": "taq_Latn",
    "Tamasheq Tifinagh": "taq_Tfng",
    "Tok Pisin": "tpi_Latn",
    "Tswana": "tsn_Latn",
    "Tsonga": "tso_Latn",
    "Turkmen": "tuk_Latn",
    "Tumbuka": "tum_Latn",
    "Turkish": "tur_Latn",
    "Twi": "twi_Latn",
    "Central Atlas Tamazight": "tzm_Tfng",
    "Uyghur": "uig_Arab",
    "Ukrainian": "ukr_Cyrl",
    "Umbundu": "umb_Latn",
    "Urdu": "urd_Arab",
    "Northern Uzbek": "uzn_Latn",
    "Venetian": "vec_Latn",
    "Vietnamese": "vie_Latn",
    "Waray": "war_Latn",
    "Wolof": "wol_Latn",
    "Xhosa": "xho_Latn",
    "Eastern Yiddish": "ydd_Hebr",
    "Yoruba": "yor_Latn",
    "Yue Chinese": "yue_Hant",
    "Chinese Simplified": "zho_Hans",
    "Chinese Traditional": "zho_Hant",
    "Standard Malay": "zsm_Latn",
    "Zulu": "zul_Latn",
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Any							="facebook/nllb-200-distilled-600M"
     lowerCamelCase  :					Optional[Any]							=(
         "This is a tool that translates text from a language to another. It takes three inputs: `text`, which should "
         "be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, "
         "which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in "
         "plain English, such as 'Romanian', or 'Albanian'. It returns the text translated in `tgt_lang`."
     )
     lowerCamelCase  :					Tuple							="translator"
     lowerCamelCase  :					Any							=AutoTokenizer
     lowerCamelCase  :					Dict							=AutoModelForSeqaSeqLM
     lowerCamelCase  :					Union[str, Any]							=LANGUAGE_CODES

     lowerCamelCase  :					Any							=["text", "text", "text"]
     lowerCamelCase  :					Union[str, Any]							=["text"]






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		Any  ,    a		:		List[str]  ,    a		:		Any   ):





         """simple docstring"""



         if src_lang not in self.lang_to_code:
             raise ValueError(f"""{src_lang} is not a supported language."""   )
         if tgt_lang not in self.lang_to_code:
             raise ValueError(f"""{tgt_lang} is not a supported language."""   )
         __lowerCamelCase          =  self.lang_to_code[src_lang]
         __lowerCamelCase          =  self.lang_to_code[tgt_lang]
         return self.pre_processor._build_translation_inputs(
             a  ,    return_tensors='''pt'''  ,    src_lang=a  ,    tgt_lang=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         return self.model.generate(**a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		str   ):





         """simple docstring"""



         return self.post_processor.decode(outputs[0].tolist()  ,    skip_special_tokens=a   )
