

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


a_     :    Union[str, Any]     		=   logging.get_logger(__name__)

a_     :    str     		=   "▁"

a_     :    str     		=   {"vocab_file": "sentencepiece.bpe.model"}

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

a_     :    Optional[int]     		=   {
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
a_     :    Union[str, 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   UpperCamelCase			(  __A ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =["input_ids", "attention_mask"]

    __UpperCamelCase    =[]
    __UpperCamelCase    =[]

    def __init__(      self			:   Tuple				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   Optional[int]="<s>"				,     snake_case__			:   Optional[int]="</s>"				,     snake_case__			:   Any="</s>"				,     snake_case__			:   Optional[Any]="<s>"				,     snake_case__			:   Optional[int]="<unk>"				,     snake_case__			:   Any="<pad>"				,     snake_case__			:   List[str]="<mask>"				,     snake_case__			:   Any=None				,     snake_case__			:   Any=None				,     snake_case__			:   Any=None				,     snake_case__			:   Tuple = None				,     snake_case__			:   Any=None				,     snake_case__			:   str=False				,     **snake_case__			:   int				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(__A				,     lstrip=__A				,     rstrip=__A     ) if isinstance(__A				,     __A     ) else mask_token

     SCREAMING_SNAKE_CASE		    =      {} if sp_model_kwargs is None else sp_model_kwargs
     SCREAMING_SNAKE_CASE		    =      legacy_behaviour

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

     SCREAMING_SNAKE_CASE		    =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(str(__A     )     )
     SCREAMING_SNAKE_CASE		    =      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		    =      {'<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		    =      1

     SCREAMING_SNAKE_CASE		    =      len(self.sp_model     )
     SCREAMING_SNAKE_CASE		    =      {
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A     )
     }
     SCREAMING_SNAKE_CASE		    =      {v: k for k, v in self.lang_code_to_id.items()}
     SCREAMING_SNAKE_CASE		    =      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		    =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     SCREAMING_SNAKE_CASE		    =      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		    =      src_lang if src_lang is not None else 'eng_Latn'
     SCREAMING_SNAKE_CASE		    =      self.lang_code_to_id[self._src_lang]
     SCREAMING_SNAKE_CASE		    =      tgt_lang
     self.set_src_lang_special_tokens(self._src_lang     )

    def __getstate__(      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.__dict__.copy()
     SCREAMING_SNAKE_CASE		    =      None
     SCREAMING_SNAKE_CASE		    =      self.sp_model.serialized_model_proto()
     return state

    def __setstate__(      self			:   Optional[Any]				,     snake_case__			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      d

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

     SCREAMING_SNAKE_CASE		    =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )

    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token

    @property
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     return self._src_lang

    @src_lang.setter
    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      new_src_lang
     self.set_src_lang_special_tokens(self._src_lang     )

    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[Any] = None				,     snake_case__			:   Any = False     ):

     """simple docstring"""






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

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

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Tuple				,     snake_case__			:   List[Any] = None     ):

     """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			:   Optional[int]				,     snake_case__			:   Optional[int]				,     snake_case__			:   List[str] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [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			:   List[Any]				,     snake_case__			:   Tuple				,     snake_case__			:   str				,     snake_case__			:   int				,     snake_case__			:   Optional[Any]				,     **snake_case__			:   List[str]     ):

     """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		    =      src_lang
     SCREAMING_SNAKE_CASE		    =      self(__A				,     add_special_tokens=__A				,     return_tensors=__A				,     **__A     )
     SCREAMING_SNAKE_CASE		    =      self.convert_tokens_to_ids(__A     )
     SCREAMING_SNAKE_CASE		    =      tgt_lang_id
     return inputs

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      {self.convert_ids_to_tokens(__A     ): i for i in range(self.vocab_size     )}
     vocab.update(self.added_tokens_encoder     )
     return vocab

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Any     ):

     """simple docstring"""






     return self.sp_model.encode(__A				,     out_type=__A     )

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
     SCREAMING_SNAKE_CASE		    =      self.sp_model.PieceToId(__A     )

     # 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 UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Any     ):

     """simple docstring"""






     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 UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ''.join(__A     ).replace(__A				,     ' '     ).strip()
     return out_string

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Union[str, Any] = None     ):

     """simple docstring"""






     if not os.path.isdir(__A     ):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
      return
     SCREAMING_SNAKE_CASE		    =      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     ) and os.path.isfile(self.vocab_file     ):
      copyfile(self.vocab_file				,     __A     )
     elif not os.path.isfile(self.vocab_file     ):
      with open(__A				,     'wb'     ) as fi:
       SCREAMING_SNAKE_CASE		    =      self.sp_model.serialized_model_proto()
       fi.write(__A     )

     return (out_vocab_file,)

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Any = "eng_Latn"				,     snake_case__			:   int = None				,     snake_case__			:   Tuple = "fra_Latn"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      src_lang
     SCREAMING_SNAKE_CASE		    =      tgt_lang
     return super().prepare_seqaseq_batch(__A				,     __A				,     **__A     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return self.set_src_lang_special_tokens(self.src_lang     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     return self.set_tgt_lang_special_tokens(self.tgt_lang     )

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.lang_code_to_id[src_lang]
     if self.legacy_behaviour:
      SCREAMING_SNAKE_CASE		    =      []
      SCREAMING_SNAKE_CASE		    =      [self.eos_token_id, self.cur_lang_code]
     else:
      SCREAMING_SNAKE_CASE		    =      [self.cur_lang_code]
      SCREAMING_SNAKE_CASE		    =      [self.eos_token_id]




    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.lang_code_to_id[lang]
     if self.legacy_behaviour:
      SCREAMING_SNAKE_CASE		    =      []
      SCREAMING_SNAKE_CASE		    =      [self.eos_token_id, self.cur_lang_code]
     else:
      SCREAMING_SNAKE_CASE		    =      [self.cur_lang_code]
      SCREAMING_SNAKE_CASE		    =      [self.eos_token_id]





import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""--model_ckpt"""		,				type=lowerCAmelCase__		,				default="""microsoft/unixcoder-base-nine"""   )
   parser.add_argument("""--num_epochs"""		,				type=lowerCAmelCase__		,				default=5   )
   parser.add_argument("""--batch_size"""		,				type=lowerCAmelCase__		,				default=6   )
   parser.add_argument("""--gradient_accumulation_steps"""		,				type=lowerCAmelCase__		,				default=1   )
   parser.add_argument("""--freeze"""		,				type=lowerCAmelCase__		,				default=lowerCAmelCase__   )
   parser.add_argument("""--learning_rate"""		,				type=lowerCAmelCase__		,				default=5E-4   )
   parser.add_argument("""--seed"""		,				type=lowerCAmelCase__		,				default=0   )
   parser.add_argument("""--lr_scheduler_type"""		,				type=lowerCAmelCase__		,				default="""cosine"""   )
   parser.add_argument("""--num_warmup_steps"""		,				type=lowerCAmelCase__		,				default=10   )
   parser.add_argument("""--weight_decay"""		,				type=lowerCAmelCase__		,				default=0.0_1   )
   parser.add_argument("""--output_dir"""		,				type=lowerCAmelCase__		,				default="""./results"""   )
   return parser.parse_args()


SCREAMING_SNAKE_CASE							    =							load('accuracy')




def  a   (lowerCAmelCase__   ):
   __a	,    __a					=  eval_pred
   __a					=  np.argmax(lowerCAmelCase__		,				axis=1   )
   return metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  trainer


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if control.should_evaluate:
           __a					=  deepcopy(__A    )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,       metric_key_prefix="""train"""    )
           return control_copy







def  a   ():
   __a					=  get_args()
   set_seed(args.seed   )

   __a					=  load_dataset("""codeparrot/codecomplex"""		,				split="""train"""   )
   __a					=  dataset.train_test_split(test_size=0.2   )
   __a					=  train_test["""test"""].train_test_split(test_size=0.5   )
   __a					=  DatasetDict(
       {
           """train""": train_test["""train"""],
           """test""": test_validation["""train"""],
           """valid""": test_validation["""test"""],
       }   )

   print("""Loading tokenizer and model"""   )
   __a					=  AutoTokenizer.from_pretrained(args.model_ckpt   )
   __a					=  tokenizer.eos_token
   __a					=  AutoModelForSequenceClassification.from_pretrained(args.model_ckpt		,				num_labels=7   )
   __a					=  model.config.eos_token_id

   if args.freeze:
      for param in model.roberta.parameters():
         __a					=  False

   __a					=  ClassLabel(num_classes=7		,				names=list(set(train_test_validation["""train"""]["""complexity"""]   )   )   )

   def tokenize(lowerCAmelCase__   ):
      __a					=  tokenizer(example["""src"""]		,				truncation=lowerCAmelCase__		,				max_length=1_024   )
      __a					=  labels.straint(example["""complexity"""]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

   __a					=  train_test_validation.map(
       lowerCAmelCase__		,				batched=lowerCAmelCase__		,				remove_columns=train_test_validation["""train"""].column_names		,				)
   __a					=  DataCollatorWithPadding(tokenizer=lowerCAmelCase__   )

   __a					=  TrainingArguments(
       output_dir=args.output_dir		,				learning_rate=args.learning_rate		,				lr_scheduler_type=args.lr_scheduler_type		,				evaluation_strategy="""epoch"""		,				save_strategy="""epoch"""		,				logging_strategy="""epoch"""		,				per_device_train_batch_size=args.batch_size		,				per_device_eval_batch_size=args.batch_size		,				num_train_epochs=args.num_epochs		,				gradient_accumulation_steps=args.gradient_accumulation_steps		,				weight_decay=0.0_1		,				metric_for_best_model="""accuracy"""		,				run_name="""complexity-java"""		,				report_to="""wandb"""		,				)

   __a					=  Trainer(
       model=lowerCAmelCase__		,				args=lowerCAmelCase__		,				train_dataset=tokenized_datasets["""train"""]		,				eval_dataset=tokenized_datasets["""valid"""]		,				tokenizer=lowerCAmelCase__		,				data_collator=lowerCAmelCase__		,				compute_metrics=lowerCAmelCase__		,				)

   print("""Training..."""   )
   trainer.add_callback(CustomCallback(lowerCAmelCase__   )   )
   trainer.train()


if __name__ == "__main__":
  main()



