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


lowercase__       =logging.getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				List[Any]  ):
 if metric == "rouge2":
  __a :  Dict						=  '{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  __a :  List[str]						=  '{val_avg_bleu:.4f}-{step_count}'
 elif metric == "em":
  __a :  str						=  '{val_avg_em:.4f}-{step_count}'
 elif metric == "loss":
  __a :  Dict						=  '{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 :  List[str]						=  ModelCheckpoint(
     dirpath=UpperCAmelCase_  ,   filename=UpperCAmelCase_  ,   monitor=f"val_{metric}"  ,   mode='''max'''  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

 return checkpoint_callback




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ,   lowerCAmelCase__							:				List[str]  ):
 return EarlyStopping(
     monitor=f"val_{metric}"  ,   mode='''min''' if '''loss''' in metric else '''max'''  ,   patience=UpperCAmelCase_  ,   verbose=UpperCAmelCase_  ,   )



class 		UpperCamelCase__  (       pl.Callback    ):
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]   ):
    __a :  Any						=  {f"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
    pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_   )
   @rank_zero_only
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict=True   ):
    logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"   )
    __a :  List[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
    __a :  Optional[Any]						=  Path(pl_module.hparams.output_dir   )
    if type_path == "test":
     __a :  int						=  od / 'test_results.txt'
     __a :  List[str]						=  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 :  Any						=  od / f"{type_path}_results/{trainer.global_step:05d}.txt"
     __a :  Any						=  od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
     results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_   )
     generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_   )
    with open(SCREAMING_SNAKE_CASE_					,				'''a+'''   ) as writer:
     for key in sorted(SCREAMING_SNAKE_CASE_   ):
      if key in ["log", "progress_bar", "preds"]:
       continue
      __a :  Any						=  metrics[key]
      if isinstance(SCREAMING_SNAKE_CASE_					,				torch.Tensor   ):
       __a :  List[Any]						=  val.item()
      __a :  Optional[int]						=  f"{key}: {val:.6f}\n"
      writer.write(SCREAMING_SNAKE_CASE_   )

    if not save_generations:
     return

    if "preds" in metrics:
     __a :  str						=  '\n'.join(metrics['''preds''']   )
     generations_file.open('''w+'''   ).write(SCREAMING_SNAKE_CASE_   )
   @rank_zero_only
   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					List[str]					,				snake_case_ :					Optional[int]   ):
    try:
     __a :  Optional[int]						=  pl_module.model.model.num_parameters()
    except AttributeError:
     __a :  Tuple						=  pl_module.model.num_parameters()

    __a :  Union[str, Any]						=  count_trainable_parameters(SCREAMING_SNAKE_CASE_   )
    # 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 :					Any					,				snake_case_ :					int					,				snake_case_ :					Optional[Any]   ):
    save_json(pl_module.metrics					,				pl_module.metrics_save_path   )
    return self._write_logs(SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				'''test'''   )







   @rank_zero_only
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					Any					,				snake_case_ :					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")




'''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_xlnet import XLNetTokenizer
else:
      A__		:					Dict												=  None


A__		:					List[Any]												=  logging.get_logger(__name__)

A__		:					str												=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A__		:					Union[str, Any]												=  {
    """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""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

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

A__		:					Dict												=  """▁"""

# Segments (not really needed)
A__		:					List[str]												=  0
A__		:					List[Any]												=  1
A__		:					Union[str, Any]												=  2
A__		:					List[Any]												=  3
A__		:					str												=  4





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					str							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Dict							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Tuple							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					List[Any]							     =			'left'
    lowerCamelCase  :					Optional[Any]							     =			XLNetTokenizer







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=None			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<sep>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_="<cls>"			,     SCREAMING_SNAKE_CASE_="<mask>"			,     SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"]			,     **SCREAMING_SNAKE_CASE_			,     )				->     Union[str, Any]:
         # Mask token behave like a normal word, i.e. include the space before it
         __lowerCamelCase   :	str								=		AddedToken(SCREAMING_SNAKE_CASE_			,     lstrip=SCREAMING_SNAKE_CASE_			,     rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	) else mask_token

         super().__init__(
             vocab_file=SCREAMING_SNAKE_CASE_			,     tokenizer_file=SCREAMING_SNAKE_CASE_			,     do_lower_case=SCREAMING_SNAKE_CASE_			,     remove_space=SCREAMING_SNAKE_CASE_			,     keep_accents=SCREAMING_SNAKE_CASE_			,     bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     sep_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     cls_token=SCREAMING_SNAKE_CASE_			,     mask_token=SCREAMING_SNAKE_CASE_			,     additional_special_tokens=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	Union[str, Any]								=		3
         __lowerCamelCase   :	int								=		do_lower_case
         __lowerCamelCase   :	Optional[Any]								=		remove_space
         __lowerCamelCase   :	int								=		keep_accents
         __lowerCamelCase   :	Any								=		vocab_file
         __lowerCamelCase   :	Any								=		False if not self.vocab_file else True







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	Optional[Any]								=		[self.sep_token_id]
         __lowerCamelCase   :	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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     List[int]:
         __lowerCamelCase   :	int								=		[self.sep_token_id]
         __lowerCamelCase   :	Dict								=		[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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = 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(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

         return (out_vocab_file,)






