

"""simple docstring"""






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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    filter(lambda _lowerCamelCase   : p.requires_grad ,   model.parameters()   )
    _lowerCAmelCase       :   Any  =    sum([np.prod(p.size()   ) for p in model_parameters]   )
    return params


_lowerCAmelCase						=		logging.getLogger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if metric == "rouge2":
        _lowerCAmelCase       :   Dict  =    '{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _lowerCAmelCase       :   int  =    '{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _lowerCAmelCase       :   List[str]  =    '{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       :   Union[str, Any]  =    ModelCheckpoint(
        dirpath=_lowerCamelCase ,   filename=_lowerCamelCase ,   monitor=f"""val_{metric}""" ,   mode='max' ,   save_top_k=3 ,   every_n_epochs=1 ,   )
    return checkpoint_callback

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    return EarlyStopping(
        monitor=f"""val_{metric}""" ,   mode='min' if 'loss' in metric else 'max' ,   patience=_lowerCamelCase ,   verbose=_lowerCamelCase ,   )







class 	__UpperCamelCase    (    pl.Callback						):

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
      pl_module.logger.log_metrics(_A      )

  @rank_zero_only
  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A=True      ):
      '''simple docstring'''

      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
      _lowerCAmelCase       :   Union[str, Any]  =    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       :   List[str]  =    Path(pl_module.hparams.output_dir      )
      if type_path == "test":
          _lowerCAmelCase       :   str  =    od / 'test_results.txt'
          _lowerCAmelCase       :   Dict  =    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       :   Union[str, Any]  =    od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
          _lowerCAmelCase       :   Dict  =    od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
          results_file.parent.mkdir(exist_ok=_A      )
          generations_file.parent.mkdir(exist_ok=_A      )
      with open(_A    ,'a+'      ) as writer:
          for key in sorted(_A      ):
              if key in ["log", "progress_bar", "preds"]:
                  continue
              _lowerCAmelCase       :   Union[str, Any]  =    metrics[key]
              if isinstance(_A    ,torch.Tensor      ):
                  _lowerCAmelCase       :   int  =    val.item()
              _lowerCAmelCase       :   Optional[Any]  =    F"""{key}: {val:.6f}\n"""
              writer.write(_A      )

      if not save_generations:
          return

      if "preds" in metrics:
          _lowerCAmelCase       :   Optional[Any]  =    '\n'.join(metrics['preds']      )
          generations_file.open('w+'      ).write(_A      )

  @rank_zero_only
  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      try:
          _lowerCAmelCase       :   Any  =    pl_module.model.model.num_parameters()
      except AttributeError:
          _lowerCAmelCase       :   Any  =    pl_module.model.num_parameters()

      _lowerCAmelCase       :   Optional[int]  =    count_trainable_parameters(_A      )
      # 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 __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      save_json(pl_module.metrics    ,pl_module.metrics_save_path      )
      return self._write_logs(_A    ,_A    ,'test'      )







  @rank_zero_only
  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      save_json(pl_module.metrics    ,pl_module.metrics_save_path      )
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")








"""simple docstring"""






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{"""vocab_file""": """spiece.model"""}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

_lowerCAmelCase						=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
_lowerCAmelCase						=		0
_lowerCAmelCase						=		1
_lowerCAmelCase						=		2
_lowerCAmelCase						=		3
_lowerCAmelCase						=		4







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		"left"

  def __init__(		self    ,_A    ,_A=False    ,_A=True    ,_A=False    ,_A="<s>"    ,_A="</s>"    ,_A="<unk>"    ,_A="<sep>"    ,_A="<pad>"    ,_A="<cls>"    ,_A="<mask>"    ,_A=["<eop>", "<eod>"]    ,_A = None    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    AddedToken(_A    ,lstrip=_A    ,rstrip=_A      ) if isinstance(_A    ,_A      ) else mask_token

      _lowerCAmelCase       :   Union[str, Any]  =    {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=_A    ,remove_space=_A    ,keep_accents=_A    ,bos_token=_A    ,eos_token=_A    ,unk_token=_A    ,sep_token=_A    ,pad_token=_A    ,cls_token=_A    ,mask_token=_A    ,additional_special_tokens=_A    ,sp_model_kwargs=self.sp_model_kwargs    ,**_A    ,)

      _lowerCAmelCase       :   int  =    3

      _lowerCAmelCase       :   Union[str, Any]  =    do_lower_case
      _lowerCAmelCase       :   Dict  =    remove_space
      _lowerCAmelCase       :   int  =    keep_accents
      _lowerCAmelCase       :   List[str]  =    vocab_file

      _lowerCAmelCase       :   List[Any]  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(_A      )

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.sp_model      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab

  def __getstate__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.__dict__.copy()
      _lowerCAmelCase       :   List[str]  =    None
      return state

  def __setstate__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    d

      # for backward compatibility
      if not hasattr(self    ,'sp_model_kwargs'      ):
          _lowerCAmelCase       :   Union[str, Any]  =    {}

      _lowerCAmelCase       :   Optional[Any]  =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if self.remove_space:
          _lowerCAmelCase       :   str  =    ' '.join(inputs.strip().split()      )
      else:
          _lowerCAmelCase       :   Dict  =    inputs
      _lowerCAmelCase       :   List[str]  =    outputs.replace('``'    ,'"'      ).replace('\'\''    ,'"'      )

      if not self.keep_accents:
          _lowerCAmelCase       :   Optional[Any]  =    unicodedata.normalize('NFKD'    ,_A      )
          _lowerCAmelCase       :   Dict  =    ''.join([c for c in outputs if not unicodedata.combining(_A      )]      )
      if self.do_lower_case:
          _lowerCAmelCase       :   Tuple  =    outputs.lower()

      return outputs

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.preprocess_text(_A      )
      _lowerCAmelCase       :   int  =    self.sp_model.encode(_A    ,out_type=_A      )
      _lowerCAmelCase       :   int  =    []
      for piece in pieces:
          if len(_A      ) > 1 and piece[-1] == str(','      ) and piece[-2].isdigit():
              _lowerCAmelCase       :   Union[str, Any]  =    self.sp_model.EncodeAsPieces(piece[:-1].replace(_A    ,''      )      )
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]      ) == 1:
                      _lowerCAmelCase       :   int  =    cur_pieces[1:]
                  else:
                      _lowerCAmelCase       :   Any  =    cur_pieces[0][1:]
              cur_pieces.append(piece[-1]      )
              new_pieces.extend(_A      )
          else:
              new_pieces.append(_A      )

      return new_pieces

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.sp_model.PieceToId(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.sp_model.IdToPiece(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    ''.join(_A      ).replace(_A    ,' '      ).strip()
      return out_string

  def __lowerCamelCase		(		self    ,_A    ,_A = False    ,_A = None    ,_A = True    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    kwargs.pop('use_source_tokenizer'    ,_A      )

      _lowerCAmelCase       :   Dict  =    self.convert_ids_to_tokens(_A    ,skip_special_tokens=_A      )

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      _lowerCAmelCase       :   Optional[Any]  =    []
      _lowerCAmelCase       :   int  =    []
      for token in filtered_tokens:
          if skip_special_tokens and token in self.all_special_ids:
              continue
          if token in self.added_tokens_encoder:
              if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(_A      )      )
                  _lowerCAmelCase       :   Tuple  =    []
              sub_texts.append(_A      )
          else:
              current_sub_text.append(_A      )
      if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(_A      )      )

      # Mimic the behavior of the Rust tokenizer:
      # By default, there are no spaces between special tokens
      _lowerCAmelCase       :   List[Any]  =    ''.join(_A      )

      _lowerCAmelCase       :   Tuple  =    (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
          _lowerCAmelCase       :   int  =    self.clean_up_tokenization(_A      )
          return clean_text
      else:
          return text

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    [self.sep_token_id]
      _lowerCAmelCase       :   Union[str, Any]  =    [self.cls_token_id]
      if token_ids_a is None:
          return token_ids_a + sep + cls
      return token_ids_a + sep + token_ids_a + sep + cls

  def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = 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      )

      if token_ids_a is not None:
          return ([0] * len(_A      )) + [1] + ([0] * len(_A      )) + [1, 1]
      return ([0] * len(_A      )) + [1, 1]

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    [self.sep_token_id]
      _lowerCAmelCase       :   Any  =    [2]

      if token_ids_a is None:
          return len(token_ids_a + sep      ) * [0] + cls_segment_id
      return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   str  =    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:
              _lowerCAmelCase       :   Optional[Any]  =    self.sp_model.serialized_model_proto()
              fi.write(_A      )

      return (out_vocab_file,)





