


import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
   from ...tokenization_utils_base import TextInput
from ...utils import logging


_A			=						logging.get_logger(__name__)

_A			=						{'vocab_file': 'spiece.model'}

_A			=						{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_A			=						{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

_A			=						'▁'






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		List[str]              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Tuple              =  ["input_ids", "attention_mask"]






    def __init__(      self ,       A_ ,       A_="</s>" ,       A_="<unk>" ,       A_="<pad>" ,       A_=100 ,       A_=None ,       A_ = None ,       A_=True ,       **A_ ,       )  ->	None:
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
              __UpperCamelCase   							=[f'<extra_id_{i}>' for i in range(A_   )]
         elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              __UpperCamelCase   							=len(set(filter(lambda A_   : bool('extra_id' in str(A_   )   ) ,       A_   )   )   )
              if extra_tokens != extra_ids:
                   raise ValueError(
                       f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                       ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                       ' tokens'   )
         if legacy:
              logger.warning_once(
                  f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                  ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )

         __UpperCamelCase   							=legacy
         __UpperCamelCase   							={} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=A_ ,       unk_token=A_ ,       pad_token=A_ ,       extra_ids=A_ ,       additional_special_tokens=A_ ,       sp_model_kwargs=self.sp_model_kwargs ,       legacy=A_ ,       **A_ ,       )

         __UpperCamelCase   							=vocab_file
         __UpperCamelCase   							=extra_ids

         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(A_   )






    @staticmethod
    def _a (      A_ ,       A_ ,       A_   )  ->	List[str]:
         if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
              __UpperCamelCase   							=TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
              if init_max_model_length is not None and init_max_model_length != max_model_length:
                   return init_max_model_length
              elif init_max_model_length is None:
                   warnings.warn(
                       'This tokenizer was incorrectly instantiated with a model max length of'
                       f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                       ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                       ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                       f' {pretrained_model_name_or_path} automatically truncating your input to'
                       f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                       f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                       ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                       ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,       A_ ,       )

         return max_model_length






    @property
    def _a (      self   )  ->	str:
         return self.sp_model.get_piece_size() + self._extra_ids






    def _a (      self   )  ->	int:
         __UpperCamelCase   							={self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






    def _a (      self ,       A_ ,       A_ = None ,       A_ = False   )  ->	List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=A_ ,       token_ids_a=A_ ,       already_has_special_tokens=A_   )

         # normal case: some special tokens
         if token_ids_a is None:
              return ([0] * len(A_   )) + [1]
         return ([0] * len(A_   )) + [1] + ([0] * len(A_   )) + [1]






    def _a (      self   )  ->	str:
         return list(
             set(filter(lambda A_   : bool(re.search(r'<extra_id_\d+>' ,       A_   )   ) is not None ,       self.additional_special_tokens   )   )   )






    def _a (      self   )  ->	List[Any]:
         return [self._convert_token_to_id(A_   ) for token in self.get_sentinel_tokens()]






    def _a (      self ,       A_   )  ->	List[int]:
         if len(A_   ) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                  ' eos tokens being added.'   )
              return token_ids
         else:
              return token_ids + [self.eos_token_id]






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.eos_token_id]

         if token_ids_a is None:
              return len(token_ids_a + eos   ) * [0]
         return len(token_ids_a + eos + token_ids_a + eos   ) * [0]






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=self._add_eos_if_not_present(A_   )
         if token_ids_a is None:
              return token_ids_a
         else:
              __UpperCamelCase   							=self._add_eos_if_not_present(A_   )
              return token_ids_a + token_ids_a






    def __getstate__(      self   )  ->	Optional[int]:
         __UpperCamelCase   							=self.__dict__.copy()
         __UpperCamelCase   							=None
         return state






    def __setstate__(      self ,       A_   )  ->	Optional[Any]:
         __UpperCamelCase   							=d

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

         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )






    def _a (      self ,       A_ ,       **A_   )  ->	List[str]:
         # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
         # the beginning of the text
         if not self.legacy:
              __UpperCamelCase   							=SPIECE_UNDERLINE + text.replace(A_ ,       ' '   )
         return super().tokenize(A_ ,       **A_   )






    def _a (      self ,       A_ ,       **A_   )  ->	int:
         if not self.legacy:
              __UpperCamelCase   							=text.startswith(A_   )
              if is_first:
                   __UpperCamelCase   							=text[1:]

         __UpperCamelCase   							=self.sp_model.encode(A_ ,       out_type=A_   )

         if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(A_   ):
              __UpperCamelCase   							=([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
         return tokens






    def _a (      self ,       A_   )  ->	List[str]:
         if token.startswith('<extra_id_'   ):
              __UpperCamelCase   							=re.match(r'<extra_id_(\d+)>' ,       A_   )
              __UpperCamelCase   							=int(match.group(1   )   )
              return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(A_   )






    def _a (      self ,       A_   )  ->	Optional[int]:
         if index < self.sp_model.get_piece_size():
              __UpperCamelCase   							=self.sp_model.IdToPiece(A_   )
         else:
              __UpperCamelCase   							=f'<extra_id_{self.vocab_size - 1 - index}>'
         return token






    def _a (      self ,       A_   )  ->	Tuple:
         __UpperCamelCase   							=[]
         __UpperCamelCase   							=''
         __UpperCamelCase   							=False
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   if not prev_is_special:
                        out_string += " "
                   out_string += self.sp_model.decode(A_   ) + token
                   __UpperCamelCase   							=True
                   __UpperCamelCase   							=[]
              else:
                   current_sub_tokens.append(A_   )
                   __UpperCamelCase   							=False
         out_string += self.sp_model.decode(A_   )
         return out_string.strip()






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         if not os.path.isdir(A_   ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
              return
         __UpperCamelCase   							=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:
                   __UpperCamelCase   							=self.sp_model.serialized_model_proto()
                   fi.write(A_   )

         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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
     __UpperCamelCase   							=filter(lambda SCREAMING_SNAKE_CASE__    : p.requires_grad      ,					model.parameters()    )
     __UpperCamelCase   							=sum([np.prod(p.size()    ) for p in model_parameters]    )
     return params


_A			=						logging.getLogger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     if metric == "rouge2":
          __UpperCamelCase   							='{val_avg_rouge2:.4f}-{step_count}'
     elif metric == "bleu":
          __UpperCamelCase   							='{val_avg_bleu:.4f}-{step_count}'
     elif metric == "em":
          __UpperCamelCase   							='{val_avg_em:.4f}-{step_count}'
     elif metric == "loss":
          __UpperCamelCase   							='{val_avg_loss:.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.'    )

     __UpperCamelCase   							=ModelCheckpoint(
         dirpath=SCREAMING_SNAKE_CASE__      ,					filename=SCREAMING_SNAKE_CASE__      ,					monitor=F'val_{metric}'      ,					mode='max'      ,					save_top_k=1      ,					every_n_epochs=1      ,					)

     return checkpoint_callback

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






class    UpperCAmelCase__	( pl.Callback		):



    """simple docstring"""






    def _a (      self ,       A_ ,       A_   )  ->	int:
         __UpperCamelCase   							={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 _a (      self ,       A_ ,       A_ ,       A_ ,       A_=True   )  ->	None:
         logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'   )
         __UpperCamelCase   							=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
         __UpperCamelCase   							=Path(pl_module.hparams.output_dir   )
         if type_path == "test":
              __UpperCamelCase   							=od / 'test_results.txt'
              __UpperCamelCase   							=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.
              __UpperCamelCase   							=od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              __UpperCamelCase   							=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
                   __UpperCamelCase   							=metrics[key]
                   if isinstance(A_ ,       torch.Tensor   ):
                        __UpperCamelCase   							=val.item()
                   __UpperCamelCase   							=f'{key}: {val:.6f}\n'
                   writer.write(A_   )

         if not save_generations:
              return

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






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	Optional[int]:
         try:
              __UpperCamelCase   							=pl_module.model.model.num_parameters()
         except AttributeError:
              __UpperCamelCase   							=pl_module.model.num_parameters()

         __UpperCamelCase   							=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 _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         return self._write_logs(A_ ,       A_ ,       'test'   )






    @rank_zero_only
    def _a (      self ,       A_ ,       A_   )  ->	List[str]:
         save_json(pl_module.metrics ,       pl_module.metrics_save_path   )
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





