

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 				snake_case_(_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






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


__A      		=     logging.getLogger(__name__)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  if metric == "rouge2":
    _snake_case						=							'''{val_avg_rouge2:.4f}-{step_count}'''
  elif metric == "bleu":
    _snake_case						=							'''{val_avg_bleu:.4f}-{step_count}'''
  elif metric == "em":
    _snake_case						=							'''{val_avg_em:.4f}-{step_count}'''
  elif metric == "loss":
    _snake_case						=							'''{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.'''					)

  _snake_case						=							ModelCheckpoint(
      dirpath=_UpperCamelCase			,	filename=_UpperCamelCase			,	monitor=F"""val_{metric}"""			,	mode='''max'''			,	save_top_k=1			,	every_n_epochs=1			,	)

  return checkpoint_callback



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






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



class   lowercase_   (  pl.Callback					):



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Optional[Any]      , A__				:    Optional[int]		)   ->					List[str]:
        _snake_case						=							{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 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    pl.Trainer      , A__				:    pl.LightningModule      , A__				:    str      , A__				:    List[str]=True		)   ->					None:
        logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""		)
        _snake_case						=							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						=							Path(pl_module.hparams.output_dir		)
        if type_path == "test":
          _snake_case						=							od / '''test_results.txt'''
          _snake_case						=							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						=							od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
          _snake_case						=							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
            _snake_case						=							metrics[key]
            if isinstance(A__      , torch.Tensor		):
              _snake_case						=							val.item()
            _snake_case						=							f"""{key}: {val:.6f}\n"""
            writer.write(A__		)

        if not save_generations:
          return

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



      @rank_zero_only
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Any      , A__				:    List[str]		)   ->					Any:
        try:
          _snake_case						=							pl_module.model.model.num_parameters()
        except AttributeError:
          _snake_case						=							pl_module.model.num_parameters()

        _snake_case						=							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 						UpperCamelCase_		(      self				:    List[str]      , A__				:    pl.Trainer      , A__				:    pl.LightningModule		)   ->					Union[str, Any]:
        save_json(pl_module.metrics      , pl_module.metrics_save_path		)
        return self._write_logs(A__      , A__      , '''test'''		)







      @rank_zero_only
      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    pl.Trainer      , A__				:    Optional[Any]		)   ->					Union[str, Any]:
        save_json(pl_module.metrics      , pl_module.metrics_save_path		)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[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				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

