

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:
     SCREAMING_SNAKE_CASE_     :			Dict	  =			None


SCREAMING_SNAKE_CASE_     :			int	  =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_     :			int	  =			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			{
    '''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'''
        ),
    },
}

SCREAMING_SNAKE_CASE_     :			Any	  =			{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			['''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       (			__a					):
      __a								=	VOCAB_FILES_NAMES
      __a								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a								=	PRETRAINED_VOCAB_FILES_MAP
      __a								=	['input_ids', 'attention_mask']
      __a								=	NllbTokenizer

      __a								=	[]
      __a								=	[]





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__="<s>"    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="</s>"    ,						SCREAMING_SNAKE_CASE__="<s>"    ,						SCREAMING_SNAKE_CASE__="<unk>"    ,						SCREAMING_SNAKE_CASE__="<pad>"    ,						SCREAMING_SNAKE_CASE__="<mask>"    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=False    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Any:
         # Mask token behave like a normal word, i.e. include the space before it
         lowerCamelCase__						=			AddedToken(SCREAMING_SNAKE_CASE__    ,						lstrip=SCREAMING_SNAKE_CASE__    ,						rstrip=SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		) else mask_token
         lowerCamelCase__						=			legacy_behaviour
         super().__init__(
             vocab_file=SCREAMING_SNAKE_CASE__    ,						tokenizer_file=SCREAMING_SNAKE_CASE__    ,						bos_token=SCREAMING_SNAKE_CASE__    ,						eos_token=SCREAMING_SNAKE_CASE__    ,						sep_token=SCREAMING_SNAKE_CASE__    ,						cls_token=SCREAMING_SNAKE_CASE__    ,						unk_token=SCREAMING_SNAKE_CASE__    ,						pad_token=SCREAMING_SNAKE_CASE__    ,						mask_token=SCREAMING_SNAKE_CASE__    ,						src_lang=SCREAMING_SNAKE_CASE__    ,						tgt_lang=SCREAMING_SNAKE_CASE__    ,						additional_special_tokens=SCREAMING_SNAKE_CASE__    ,						legacy_behaviour=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)

         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(SCREAMING_SNAKE_CASE__		) 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 					_lowerCamelCase  (  self		)   ->					str:
         return self._src_lang





      @src_lang.setter
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					None:
         lowerCamelCase__						=			new_src_lang
         self.set_src_lang_special_tokens(self._src_lang		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = 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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					List[int]:
         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Any:
         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(SCREAMING_SNAKE_CASE__    ,						add_special_tokens=SCREAMING_SNAKE_CASE__    ,						return_tensors=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			tgt_lang_id
         return inputs





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = "eng_Latn"    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = "fra_Latn"    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					BatchEncoding:
         lowerCamelCase__						=			src_lang
         lowerCamelCase__						=			tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         return self.set_src_lang_special_tokens(self.src_lang		)





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return self.set_tgt_lang_special_tokens(self.tgt_lang		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					None:
         lowerCamelCase__						=			self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)

         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					None:
         lowerCamelCase__						=			self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)
         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple[str]:
         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(SCREAMING_SNAKE_CASE__		):
            logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
            return
         lowerCamelCase__						=			os.path.join(
             SCREAMING_SNAKE_CASE__    ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

         return (out_vocab_file,)







"""simple docstring"""




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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)


@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )
      __a								=	field(default=__a				,       metadata={'help': 'Whether tp freeze the encoder.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Whether  to freeze the embeddings.'}					)






@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
      __a								=	field(
          default='summarization'				,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,       )
      __a								=	field(
          default=1024				,       metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=128				,       metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(default=-1				,       metadata={'help': '# training examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# validation examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# test examples. -1 means use all.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Source language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Target language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': '# num_beams to use for evaluation.'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,       )






def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Union[str, Any]:




   """simple docstring"""
   logger.info(f'***** {split} metrics *****'   )
   for key in sorted(metrics.keys()   ):
      logger.info(f'  {key} = {metrics[key]}'   )
   save_json(A__	,    os.path.join(A__	,    f'{split}_results.json'   )   )



def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # 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.

   lowerCamelCase__						=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

   if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_args_into_dataclasses()

   check_output_dir(A__   )

   # 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.parallel_mode == ParallelMode.DISTRIBUTED   )	,    training_args.fpaa	,    )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # 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()
   logger.info("Training/evaluation parameters %s"	,    A__   )

   # Set seed
   set_seed(training_args.seed   )

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

   lowerCamelCase__						=			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )

   lowerCamelCase__						=			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(A__	,    A__	,    A__   ):
         assert hasattr(A__	,    A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(A__	,    A__	,    getattr(A__	,    A__   )   )

   lowerCamelCase__						=			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	,    )
   lowerCamelCase__						=			AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path	,    from_tf=".ckpt" in model_args.model_name_or_path	,    config=A__	,    cache_dir=model_args.cache_dir	,    )

   # use task specific params
   use_task_specific_params(A__	,    data_args.task   )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowerCamelCase__						=			model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(A__	,    (MBartTokenizer, MBartTokenizerFast)   ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(A__	,    A__   ):
         lowerCamelCase__						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

   if model_args.freeze_embeds:
      freeze_embeds(A__   )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()   )
      assert_all_frozen(model.get_encoder()   )

   lowerCamelCase__						=			SeqaSeqDataset

   # Get datasets
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="train"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_train	,    max_target_length=data_args.max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_train
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="val"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_val	,    max_target_length=data_args.val_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="test"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_test	,    max_target_length=data_args.test_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowerCamelCase__						=			(
       build_compute_metrics_fn(data_args.task	,    A__   ) if training_args.predict_with_generate else None
   )
   lowerCamelCase__						=			SeqaSeqTrainer(
       model=A__	,    args=A__	,    data_args=A__	,    train_dataset=A__	,    eval_dataset=A__	,    data_collator=SeqaSeqDataCollator(
           A__	,    A__	,    model.config.decoder_start_token_id	,    training_args.tpu_num_cores   )	,    compute_metrics=A__	,    tokenizer=A__	,    )

   lowerCamelCase__						=			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"   )

      lowerCamelCase__						=			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
      lowerCamelCase__						=			train_result.metrics
      lowerCamelCase__						=			data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir	,    "trainer_state.json"   )   )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"   )

      lowerCamelCase__						=			trainer.evaluate(metric_key_prefix="val"   )
      lowerCamelCase__						=			data_args.n_val
      lowerCamelCase__						=			round(metrics["val_loss"]	,    4   )

      if trainer.is_world_process_zero():
         handle_metrics("val"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

   if training_args.do_predict:
      logger.info("*** Predict ***"   )

      lowerCamelCase__						=			trainer.predict(test_dataset=A__	,    metric_key_prefix="test"   )
      lowerCamelCase__						=			test_output.metrics
      lowerCamelCase__						=			data_args.n_test

      if trainer.is_world_process_zero():
         lowerCamelCase__						=			round(metrics["test_loss"]	,    4   )
         handle_metrics("test"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         if training_args.predict_with_generate:
            lowerCamelCase__						=			tokenizer.batch_decode(
                test_output.predictions	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
            lowerCamelCase__						=			lmap(str.strip	,    A__   )
            write_txt_file(A__	,    os.path.join(training_args.output_dir	,    "test_generations.txt"   )   )

   if trainer.is_world_process_zero():
      save_json(A__	,    os.path.join(training_args.output_dir	,    "all_results.json"   )   )

   return all_metrics



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




