


'''simple docstring'''




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_mbart import MBartTokenizer
else:
       __snake_case    						=None


__snake_case    						=logging.get_logger(__name__)


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

__snake_case    						={
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

__snake_case    						={
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
__snake_case    						=["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Dict		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Dict		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	List[str]		=     ['''input_ids''', '''attention_mask''']
     lowerCamelCase				:	List[Any]		=     MBartTokenizer

     lowerCamelCase				:	List[int]		=     []
     lowerCamelCase				:	List[int]		=     []





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      int=None    ,      UpperCAmelCase__       :      Tuple="<s>"    ,      UpperCAmelCase__       :      Tuple="</s>"    ,      UpperCAmelCase__       :      Any="</s>"    ,      UpperCAmelCase__       :      str="<s>"    ,      UpperCAmelCase__       :      str="<unk>"    ,      UpperCAmelCase__       :      Dict="<pad>"    ,      UpperCAmelCase__       :      Optional[Any]="<mask>"    ,      UpperCAmelCase__       :      int=None    ,      UpperCAmelCase__       :      List[str]=None    ,      UpperCAmelCase__       :      List[Any]=None    ,      **UpperCAmelCase__       :      str    ,      )				->  List[str]:
        # Mask token behave like a normal word, i.e. include the space before it
        lowerCAmelCase     =			AddedToken(UpperCAmelCase__    ,      lstrip=UpperCAmelCase__    ,      rstrip=UpperCAmelCase__    ) if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) else mask_token

        super().__init__(
            vocab_file=UpperCAmelCase__    ,      tokenizer_file=UpperCAmelCase__    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      sep_token=UpperCAmelCase__    ,      cls_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      pad_token=UpperCAmelCase__    ,      mask_token=UpperCAmelCase__    ,      src_lang=UpperCAmelCase__    ,      tgt_lang=UpperCAmelCase__    ,      additional_special_tokens=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )

        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(UpperCAmelCase__    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

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





     @property
     def 							__UpperCAmelCase      ( self       :      int    )				->  str:
        return self._src_lang





     @src_lang.setter
     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      str    )				->  None:
        lowerCAmelCase     =			new_src_lang
        self.set_src_lang_special_tokens(self._src_lang    )





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      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 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = 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 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str]    ,      UpperCAmelCase__       :      Optional[str]    ,      **UpperCAmelCase__       :      Tuple    )				->  Tuple:
        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(UpperCAmelCase__    ,      add_special_tokens=UpperCAmelCase__    ,      return_tensors=UpperCAmelCase__    ,      **UpperCAmelCase__    )
        lowerCAmelCase     =			self.convert_tokens_to_ids(UpperCAmelCase__    )
        lowerCAmelCase     =			tgt_lang_id
        return inputs





     def 							__UpperCAmelCase      ( self       :      str    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      str = "en_XX"    ,      UpperCAmelCase__       :      Optional[List[str]] = None    ,      UpperCAmelCase__       :      str = "ro_RO"    ,      **UpperCAmelCase__       :      Dict    ,      )				->  BatchEncoding:
        lowerCAmelCase     =			src_lang
        lowerCAmelCase     =			tgt_lang
        return super().prepare_seqaseq_batch(UpperCAmelCase__    ,      UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  List[str]:
        return self.set_src_lang_special_tokens(self.src_lang    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  List[Any]:
        return self.set_tgt_lang_special_tokens(self.tgt_lang    )





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      int    )				->  None:
        lowerCAmelCase     =			self.convert_tokens_to_ids(UpperCAmelCase__    )
        lowerCAmelCase     =			[]
        lowerCAmelCase     =			[self.eos_token_id, self.cur_lang_code]

        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 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      str    )				->  None:
        lowerCAmelCase     =			self.convert_tokens_to_ids(UpperCAmelCase__    )
        lowerCAmelCase     =			[]
        lowerCAmelCase     =			[self.eos_token_id, self.cur_lang_code]

        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 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[str] = 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(UpperCAmelCase__    ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''    )
           return
        lowerCAmelCase     =			os.path.join(
            UpperCAmelCase__    ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

        return (out_vocab_file,)












import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{val_avg_em:.4f}-{step_count}'
   else:
      raise NotImplementedError(
          F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
          ' function.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   return EarlyStopping(
       monitor=F"""val_{metric}""",	mode='min' if 'loss' in metric else 'max',	patience=lowerCAmelCase_,	verbose=lowerCAmelCase_,	)




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
     # Log results
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	od / 'test_generations.txt'
     else:
        # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
        # If people want this it will be easy enough to add back.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
        generations_file.parent.mkdir(exist_ok=lowerCAmelCase_     )
     with open(lowerCAmelCase_					,  'a+'     ) as writer:
        for key in sorted(lowerCAmelCase_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

     if "preds" in metrics:
        __lowerCAmelCase								=	'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(lowerCAmelCase_     )






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	count_trainable_parameters(lowerCAmelCase_     )
     # mp stands for million parameters
     trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}     )






  @rank_zero_only
  def 				lowercase		(				self :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

