"""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(					a							):
       __a     				=  filter(lambda a							: p.requires_grad ,  model.parameters()							)
       __a     				=  sum([np.prod(p.size()							) for p in model_parameters]							)
       return params


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



def 					_lowerCamelCase(					a ,  a							):
       if metric == "rouge2":
              __a     				=  "{val_avg_rouge2:.4f}-{step_count}"
       elif metric == "bleu":
              __a     				=  "{val_avg_bleu:.4f}-{step_count}"
       elif metric == "em":
              __a     				=  "{val_avg_em:.4f}-{step_count}"
       elif metric == "loss":
              __a     				=  "{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."							)

       __a     				=  ModelCheckpoint(
           dirpath=a ,  filename=a ,  monitor=F"val_{metric}" ,  mode="max" ,  save_top_k=1 ,  every_n_epochs=1 ,  )

       return checkpoint_callback



def 					_lowerCamelCase(					a ,  a							):
       return EarlyStopping(
           monitor=F"val_{metric}" ,  mode="min" if "loss" in metric else "max" ,  patience=a ,  verbose=a ,  )






class 				snake_case__     (   pl.Callback   ):


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  {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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase=True		):
        logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
        __a     				=  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
        __a     				=  Path(pl_module.hparams.output_dir		)
        if type_path == "test":
               __a     				=  od / "test_results.txt"
               __a     				=  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.
               __a     				=  od / F"{type_path}_results/{trainer.global_step:05d}.txt"
               __a     				=  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
                      __a     				=  metrics[key]
                      if isinstance(lowerCamelCase  ,						torch.Tensor		):
                             __a     				=  val.item()
                      __a     				=  F"{key}: {val:.6f}\n"
                      writer.write(lowerCamelCase		)

        if not save_generations:
               return

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


 @rank_zero_only
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        try:
               __a     				=  pl_module.model.model.num_parameters()
        except AttributeError:
               __a     				=  pl_module.model.num_parameters()

        __a     				=  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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        save_json(pl_module.metrics  ,						pl_module.metrics_save_path		)
        return self._write_logs(lowerCamelCase  ,						lowerCamelCase  ,						"test"		)






 @rank_zero_only
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        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
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)
