

"""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  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	filter(lambda __lowerCAmelCase							: p.requires_grad  ,					model.parameters()							)
      SCREAMING_SNAKE_CASE__   :      str      		=	sum([np.prod(p.size()							) for p in model_parameters]							)
      return params


a					:Optional[int]    = logging.getLogger(__name__)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[Any]:
      if metric == "rouge2":
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            SCREAMING_SNAKE_CASE__   :      int      		=	"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            SCREAMING_SNAKE_CASE__   :      str      		=	"""{val_avg_em:.4f}-{step_count}"""
      elif metric == "loss":
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""{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."""							)

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

      return checkpoint_callback




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      return EarlyStopping(
          monitor=F'''val_{metric}'''  ,					mode="""min""" if """loss""" in metric else """max"""  ,					patience=__lowerCAmelCase  ,					verbose=__lowerCAmelCase  ,					)



class      __a							(pl.Callback):






     '''simple docstring'''

     def 					_a			(						self	,    _a	,    _a  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[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 					_a			(						self	,    _a	,    _a	,    _a	,    _a=True  )  ->      None:


           """simple docstring"""


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

           if not save_generations:
                 return

           if "preds" in metrics:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""\n""".join(metrics["""preds"""]  )
                 generations_file.open("""w+"""  ).write(_a  )

     @rank_zero_only
     def 					_a			(						self	,    _a	,    _a  )  ->      Tuple:


           """simple docstring"""


           try:
                 SCREAMING_SNAKE_CASE__   :      int      		=	pl_module.model.model.num_parameters()
           except AttributeError:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	pl_module.model.num_parameters()

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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  )  ->      str:


           """simple docstring"""


           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  )  ->      Union[str, Any]:


           """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
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					:List[Any]    = None


a					:Optional[int]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a					:Optional[int]    = {
    "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					:Dict    = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a					:int    = "▁"

# Segments (not really needed)
a					:Dict    = 0
a					:Optional[int]    = 1
a					:Tuple    = 2
a					:List[str]    = 3
a					:Optional[Any]    = 4

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = """left"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = XLNetTokenizer

     def __init__(						self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=False	,    _a="<s>"	,    _a="</s>"	,    _a="<unk>"	,    _a="<sep>"	,    _a="<pad>"	,    _a="<cls>"	,    _a="<mask>"	,    _a=["<eop>", "<eod>"]	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

           super().__init__(
               vocab_file=_a	,    tokenizer_file=_a	,    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	,    **_a	,    )

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

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[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 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[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 					_a			(						self	,    _a	,    _a = 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(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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  ):
                 copyfile(self.vocab_file	,    _a  )

           return (out_vocab_file,)




