






"""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 import save_json



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







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


lowercase_								=	logging.getLogger(__name__)


class    snake_case						(				pl.Callback		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __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 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict=True    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     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 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	pl.LightningModule    ):

     '''simple docstring'''
     save_json(pl_module.metrics,			pl_module.metrics_save_path    )
     return self._write_logs(_lowerCamelCase,			_lowerCamelCase,			'''test'''    )






   @rank_zero_only
   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	pl.Trainer,			_lowerCamelCase					:	str    ):

     '''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")





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase=1						,			__UpperCamelCase=False					):




  """simple docstring"""







  if metric == "rouge2":
    __A		     =					'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    __A		     =					'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "loss":
    __A		     =					'''{val_avg_loss:.4f}-{step_count}'''
  else:
    raise NotImplementedError(
        f'seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'
        ''' this function.'''					)

  __A		     =					ModelCheckpoint(
      dirpath=__UpperCamelCase						,			filename=__UpperCamelCase						,			monitor=f'val_{metric}'						,			mode='''min''' if '''loss''' in metric else '''max'''						,			save_top_k=__UpperCamelCase						,			)
  return checkpoint_callback





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







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







"""simple docstring"""



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
 from .tokenization_deberta_va import DebertaVaTokenizer
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    None

SCREAMING_SNAKE_CASE_       :						int					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Any					=    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_       :						Optional[int]					=    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       VOCAB_FILES_NAMES
    UpperCAmelCase    			=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase    			=       PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase    			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase    			=       DebertaVaTokenizer

    def __init__(  self:			Optional[int]     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			str=None     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			Optional[int]=False     ,    UpperCamelCase:			List[str]="[CLS]"     ,    UpperCamelCase:			Optional[Any]="[SEP]"     ,    UpperCamelCase:			int="[UNK]"     ,    UpperCamelCase:			List[Any]="[SEP]"     ,    UpperCamelCase:			Optional[Any]="[PAD]"     ,    UpperCamelCase:			Any="[CLS]"     ,    UpperCamelCase:			List[Any]="[MASK]"     ,    **UpperCamelCase:			List[str]     ,    ):






         """simple docstring"""


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

         A__				      =	do_lower_case
         A__				      =	split_by_punct
         A__				      =	vocab_file
         A__				      =	False if not self.vocab_file else True

    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			List[Any]     ,    UpperCamelCase:			Optional[int]=None							):






         """simple docstring"""


         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 + token_ids_a + sep

    def 					UpperCamelCase						(  self:			Tuple     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			Optional[Any]=None     ,    UpperCamelCase:			Tuple=False							):






         """simple docstring"""


         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase     ,    token_ids_a=UpperCamelCase     ,    already_has_special_tokens=UpperCamelCase							)

         if token_ids_a is not None:
              return [1] + ([0] * len(UpperCamelCase							)) + [1] + ([0] * len(UpperCamelCase							)) + [1]
         return [1] + ([0] * len(UpperCamelCase							)) + [1]

    def 					UpperCamelCase						(  self:			int     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Dict=None							):






         """simple docstring"""


         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							) * [0] + len(token_ids_a + sep							) * [1]





    def 					UpperCamelCase						(  self:			Dict     ,    UpperCamelCase:			str     ,    UpperCamelCase:			Optional[str] = None							):






         """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(UpperCamelCase							):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
              return
         A__				      =	os.path.join(
             UpperCamelCase     ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

         return (out_vocab_file,)






