



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       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				)  ->     Optional[int]:

 '''simple docstring'''





 _snake_case    :  Dict					=					filter(lambda SCREAMING_SNAKE_CASE__				: p.requires_grad				,       model.parameters()				)
 _snake_case    :  List[Any]					=					sum([np.prod(p.size()				) for p in model_parameters]				)
 return params


a__    =	logging.getLogger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     List[str]:

 '''simple docstring'''





 if metric == "rouge2":
  _snake_case    :  List[Any]					=					"""{val_avg_rouge2:.4f}-{step_count}"""
 elif metric == "bleu":
  _snake_case    :  List[Any]					=					"""{val_avg_bleu:.4f}-{step_count}"""
 elif metric == "em":
  _snake_case    :  int					=					"""{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."""				)

 _snake_case    :  str					=					ModelCheckpoint(
     dirpath=SCREAMING_SNAKE_CASE__				,       filename=SCREAMING_SNAKE_CASE__				,       monitor=F'''val_{metric}'''				,       mode="""max"""				,       save_top_k=3				,       every_n_epochs=1				,       )
 return checkpoint_callback



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     str:

 '''simple docstring'''





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







class 		snake_case						(	pl.Callback  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Tuple					=					{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      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			pl.Trainer				,		lowerCAmelCase							:			pl.LightningModule				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[int]=True)				->				None:

    """simple docstring"""







    logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****''')
    _snake_case    :  str					=					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
    _snake_case    :  List[Any]					=					Path(pl_module.hparams.output_dir)
    if type_path == "test":
     _snake_case    :  List[str]					=					od / """test_results.txt"""
     _snake_case    :  Union[str, Any]					=					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.
     _snake_case    :  Union[str, Any]					=					od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
     _snake_case    :  Optional[int]					=					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
      _snake_case    :  Dict					=					metrics[key]
      if isinstance(lowerCAmelCase				,		torch.Tensor):
       _snake_case    :  str					=					val.item()
      _snake_case    :  Tuple					=					F'''{key}: {val:.6f}\n'''
      writer.write(lowerCAmelCase)

    if not save_generations:
     return

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

   @rank_zero_only
   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Optional[int])				->				Dict:

    """simple docstring"""







    try:
     _snake_case    :  Any					=					pl_module.model.model.num_parameters()
    except AttributeError:
     _snake_case    :  Optional[Any]					=					pl_module.model.num_parameters()

    _snake_case    :  int					=					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      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			pl.Trainer				,		lowerCAmelCase							:			pl.LightningModule)				->				List[str]:

    """simple docstring"""







    save_json(pl_module.metrics				,		pl_module.metrics_save_path)
    return self._write_logs(lowerCAmelCase				,		lowerCAmelCase				,		"""test""")


   @rank_zero_only
   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			pl.Trainer				,		lowerCAmelCase							:			Optional[int])				->				Optional[int]:

    """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")





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_camembert import CamembertTokenizer
else:
  a__    =	None


a__    =	logging.get_logger(__name__)

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

a__    =	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

a__    =	{
    """camembert-base""": 5_12,
}

a__    =	"""▁"""







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		VOCAB_FILES_NAMES
   snake_case_					: str          =		PRETRAINED_VOCAB_FILES_MAP
   snake_case_					: Optional[Any]          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_					: List[Any]          =		["""input_ids""", """attention_mask"""]
   snake_case_					: int          =		CamembertTokenizer

   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]=None				,		lowerCAmelCase							:			int=None				,		lowerCAmelCase							:			List[Any]="<s>"				,		lowerCAmelCase							:			List[str]="</s>"				,		lowerCAmelCase							:			Union[str, Any]="</s>"				,		lowerCAmelCase							:			Dict="<s>"				,		lowerCAmelCase							:			Dict="<unk>"				,		lowerCAmelCase							:			Any="<pad>"				,		lowerCAmelCase							:			List[str]="<mask>"				,		lowerCAmelCase							:			List[str]=["<s>NOTUSED", "</s>NOTUSED"]				,		**lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					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				,		sep_token=lowerCAmelCase				,		cls_token=lowerCAmelCase				,		unk_token=lowerCAmelCase				,		pad_token=lowerCAmelCase				,		mask_token=lowerCAmelCase				,		additional_special_tokens=lowerCAmelCase				,		**lowerCAmelCase				,		)

    _snake_case    :  List[str]					=					vocab_file
    _snake_case    :  Optional[Any]					=					False if not self.vocab_file else True

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _snake_case    :  Optional[Any]					=					[self.cls_token_id]
    _snake_case    :  List[Any]					=					[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			List[int]				,		lowerCAmelCase							:			Optional[List[int]] = None)				->				List[int]:

    """simple docstring"""







    _snake_case    :  str					=					[self.sep_token_id]
    _snake_case    :  List[str]					=					[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							:			str				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[str] = None)				->				Tuple[str]:

    """simple docstring"""







    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
    _snake_case    :  Optional[int]					=					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,)
