import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A   :     List[str]       =				logging.get_logger(__name__)

A   :     Tuple       =				"▁"

A   :     Dict       =				{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

A   :     str       =				{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

A   :     str       =				{
    "facebook/m2m100_418M": 10_24,
}

# fmt: off
A   :     Tuple       =				{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  ['''input_ids''', '''attention_mask''']

  lowerCamelCase__   =  []
  lowerCamelCase__   =  []







  def __init__(							self       :				str     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				Dict="<s>"     ,   __magic_name__       :				Union[str, Any]="</s>"     ,   __magic_name__       :				List[Any]="</s>"     ,   __magic_name__       :				int="<pad>"     ,   __magic_name__       :				Optional[Any]="<unk>"     ,   __magic_name__       :				Union[str, Any]="m2m100"     ,   __magic_name__       :				Optional[Dict[str, Any]] = None     ,   __magic_name__       :				Union[str, Any]=8     ,   **__magic_name__       :				List[Any]     ,   )     ->		None:
        SCREAMING_SNAKE_CASE_      =     {} if sp_model_kwargs is None else sp_model_kwargs

        SCREAMING_SNAKE_CASE_      =     language_codes
        SCREAMING_SNAKE_CASE_      =     FAIRSEQ_LANGUAGE_CODES[language_codes]
        SCREAMING_SNAKE_CASE_      =     {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

        SCREAMING_SNAKE_CASE_      =     kwargs.get("additional_special_tokens"     ,   []      )
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(__magic_name__      )
            for lang_code in fairseq_language_code
            if self.get_lang_token(__magic_name__      ) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=__magic_name__     ,   tgt_lang=__magic_name__     ,   bos_token=__magic_name__     ,   eos_token=__magic_name__     ,   sep_token=__magic_name__     ,   unk_token=__magic_name__     ,   pad_token=__magic_name__     ,   language_codes=__magic_name__     ,   sp_model_kwargs=self.sp_model_kwargs     ,   num_madeup_words=__magic_name__     ,   **__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     vocab_file
        SCREAMING_SNAKE_CASE_      =     load_json(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {v: k for k, v in self.encoder.items()}
        SCREAMING_SNAKE_CASE_      =     spm_file
        SCREAMING_SNAKE_CASE_      =     load_spm(__magic_name__     ,   self.sp_model_kwargs      )

        SCREAMING_SNAKE_CASE_      =     len(self.encoder      )

        SCREAMING_SNAKE_CASE_      =     {
            self.get_lang_token(__magic_name__      ): self.encoder_size + i for i, lang_code in enumerate(__magic_name__      )
        }
        SCREAMING_SNAKE_CASE_      =     {lang_code: self.encoder_size + i for i, lang_code in enumerate(__magic_name__      )}
        SCREAMING_SNAKE_CASE_      =     {v: k for k, v in self.lang_token_to_id.items()}

        SCREAMING_SNAKE_CASE_      =     src_lang if src_lang is not None else "en"
        SCREAMING_SNAKE_CASE_      =     tgt_lang
        SCREAMING_SNAKE_CASE_      =     self.get_lang_id(self._src_lang      )
        self.set_src_lang_special_tokens(self._src_lang      )

        SCREAMING_SNAKE_CASE_      =     num_madeup_words







  @property
  def       __A   (							self       :				List[str]      )     ->		int:
        return len(self.encoder      ) + len(self.lang_token_to_id      )







  @property
  def       __A   (							self       :				List[str]      )     ->		str:
        return self._src_lang







  @src_lang.setter
  def       __A   (							self       :				Dict     ,   __magic_name__       :				str      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang      )







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				str      )     ->		List[str]:
        return self.sp_model.encode(__magic_name__     ,   out_type=__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				List[Any]      )     ->		Optional[Any]:
        if token in self.lang_token_to_id:
              return self.lang_token_to_id[token]
        return self.encoder.get(__magic_name__     ,   self.encoder[self.unk_token]      )







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				int      )     ->		str:
        if index in self.id_to_lang_token:
              return self.id_to_lang_token[index]
        return self.decoder.get(__magic_name__     ,   self.unk_token      )







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     ""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(__magic_name__      ) + token
                    SCREAMING_SNAKE_CASE_      =     []
              else:
                    current_sub_tokens.append(__magic_name__      )
        out_string += self.sp_model.decode(__magic_name__      )
        return out_string.strip()







  def       __A   (							self       :				Dict     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None     ,   __magic_name__       :				bool = False      )     ->		List[int]:

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

        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(__magic_name__      )) + suffix_ones
        return prefix_ones + ([0] * len(__magic_name__      )) + ([0] * len(__magic_name__      )) + suffix_ones







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				List[int]     ,   __magic_name__       :				Optional[List[int]] = None      )     ->		List[int]:
        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       :				str      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     {self.convert_ids_to_tokens(__magic_name__      ): i for i in range(self.vocab_size      )}
        vocab.update(self.added_tokens_encoder      )
        return vocab







  def __getstate__(							self       :				List[Any]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     self.__dict__.copy()
        SCREAMING_SNAKE_CASE_      =     None
        return state







  def __setstate__(							self       :				Optional[Any]     ,   __magic_name__       :				Dict      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     d

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

        SCREAMING_SNAKE_CASE_      =     load_spm(self.spm_file     ,   self.sp_model_kwargs      )







  def       __A   (							self       :				Dict     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[str] = None      )     ->		Tuple[str]:
        SCREAMING_SNAKE_CASE_      =     Path(__magic_name__      )
        if not save_dir.is_dir():
              raise OSError(F'''{save_directory} should be a directory'''      )
        SCREAMING_SNAKE_CASE_      =     save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        SCREAMING_SNAKE_CASE_      =     save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder     ,   __magic_name__      )

        if os.path.abspath(self.spm_file      ) != os.path.abspath(__magic_name__      ) and os.path.isfile(self.spm_file      ):
              copyfile(self.spm_file     ,   __magic_name__      )
        elif not os.path.isfile(self.spm_file      ):
              with open(__magic_name__     ,   "wb"      ) as fi:
                    SCREAMING_SNAKE_CASE_      =     self.sp_model.serialized_model_proto()
                    fi.write(__magic_name__      )

        return (str(__magic_name__      ), str(__magic_name__      ))







  def       __A   (							self       :				str     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				str = "en"     ,   __magic_name__       :				Optional[List[str]] = None     ,   __magic_name__       :				str = "ro"     ,   **__magic_name__       :				Union[str, Any]     ,   )     ->		BatchEncoding:
        SCREAMING_SNAKE_CASE_      =     src_lang
        SCREAMING_SNAKE_CASE_      =     tgt_lang
        self.set_src_lang_special_tokens(self.src_lang      )
        return super().prepare_seqaseq_batch(__magic_name__     ,   __magic_name__     ,   **__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				List[str]     ,   __magic_name__       :				Optional[str]     ,   __magic_name__       :				Optional[str]     ,   **__magic_name__       :				List[Any]      )     ->		Dict:
        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(__magic_name__     ,   add_special_tokens=__magic_name__     ,   **__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_lang_id(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tgt_lang_id
        return inputs







  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        self.set_src_lang_special_tokens(self.src_lang      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        self.set_tgt_lang_special_tokens(self.tgt_lang      )







  def       __A   (							self       :				int     ,   __magic_name__       :				str      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     self.get_lang_token(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.lang_token_to_id[lang_token]
        SCREAMING_SNAKE_CASE_      =     [self.cur_lang_id]
        SCREAMING_SNAKE_CASE_      =     [self.eos_token_id]







  def       __A   (							self       :				Dict     ,   __magic_name__       :				str      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     self.get_lang_token(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.lang_token_to_id[lang_token]
        SCREAMING_SNAKE_CASE_      =     [self.cur_lang_id]
        SCREAMING_SNAKE_CASE_      =     [self.eos_token_id]







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				str      )     ->		str:
        return self.lang_code_to_token[lang]







  def       __A   (							self       :				Any     ,   __magic_name__       :				str      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.get_lang_token(__magic_name__      )
        return self.lang_token_to_id[lang_token]



def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     sentencepiece.SentencePieceProcessor(**__UpperCamelCase )
      spm.Load(str(__UpperCamelCase ) )
      return spm


def    a__       (						__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"r" ) as f:
            return json.load(__UpperCamelCase )





def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"w" ) as f:
            json.dump(__UpperCamelCase   ,					__UpperCamelCase   ,					indent=2 )



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A   :     Any       =				logging.getLogger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (preds == labels).mean()


@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,					)



@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()    )}    )
  lowerCamelCase__   =  field(metadata={'''help''': '''Should contain the data files for the task.'''}    )
  lowerCamelCase__   =  field(
      default=1_2_8     ,					metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }     ,					)
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )



def    a__       (						):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE_      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir )
          and os.listdir(training_args.output_dir )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                " --overwrite_output_dir to overcome." )

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,					datefmt="%m/%d/%Y %H:%M:%S"   ,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,					)
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"   ,					training_args.local_rank   ,					training_args.device   ,					training_args.n_gpu   ,					bool(training_args.local_rank != -1 )   ,					training_args.fpaa   ,					)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"   ,					__UpperCamelCase )

      # Set seed
      set_seed(training_args.seed )

      try:
            SCREAMING_SNAKE_CASE_      =     processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE_      =     processor.get_labels()
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase )
      except KeyError:
            raise ValueError("Task not found: %s" % (data_args.task_name) )

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=__UpperCamelCase   ,					finetuning_task=data_args.task_name   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path   ,					from_tf=bool(".ckpt" in model_args.model_name_or_path )   ,					config=__UpperCamelCase   ,					cache_dir=model_args.cache_dir   ,					)

      # Get datasets
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.train   ,					)
          if training_args.do_train
          else None
      )
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.dev   ,					)
          if training_args.do_eval
          else None
      )

      def compute_metrics(__UpperCamelCase ) -> Dict:
            SCREAMING_SNAKE_CASE_      =     np.argmax(p.predictions   ,					axis=1 )
            return {"acc": simple_accuracy(__UpperCamelCase   ,					p.label_ids )}

      # Data collator
      SCREAMING_SNAKE_CASE_      =     DataCollatorWithPadding(__UpperCamelCase   ,					pad_to_multiple_of=8 ) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_      =     Trainer(
          model=__UpperCamelCase   ,					args=__UpperCamelCase   ,					train_dataset=__UpperCamelCase   ,					eval_dataset=__UpperCamelCase   ,					compute_metrics=__UpperCamelCase   ,					data_collator=__UpperCamelCase   ,					)

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
      SCREAMING_SNAKE_CASE_      =     {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            SCREAMING_SNAKE_CASE_      =     trainer.evaluate()

            SCREAMING_SNAKE_CASE_      =     os.path.join(training_args.output_dir   ,					"eval_results.txt" )
            if trainer.is_world_master():
                  with open(__UpperCamelCase   ,					"w" ) as writer:
                        logger.info("***** Eval results *****" )
                        for key, value in result.items():
                              logger.info("  %s = %s"   ,					__UpperCamelCase   ,					__UpperCamelCase )
                              writer.write("%s = %s\n" % (key, value) )

                        results.update(__UpperCamelCase )

      return results


def    a__       (						__UpperCamelCase ):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()


