



'''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 				lowerCamelCase__	(						_A    ):
  a						: Any					       =					filter(lambda _A    : p.requires_grad			,      model.parameters()    )
  a						: List[str]					       =					sum([np.prod(p.size()    ) for p in model_parameters]    )
  return params


lowerCAmelCase: Dict							=						logging.getLogger(__name__)
def 				lowerCamelCase__	(						_A			,      _A    ):
  if metric == "rouge2":
    a						: str					       =					'{val_avg_rouge2:.4f}-{step_count}'
  elif metric == "bleu":
    a						: Any					       =					'{val_avg_bleu:.4f}-{step_count}'
  elif metric == "em":
    a						: Any					       =					'{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.'    )

  a						: List[Any]					       =					ModelCheckpoint(
      dirpath=_A			,      filename=_A			,      monitor=f"""val_{metric}"""			,      mode='max'			,      save_top_k=3			,      every_n_epochs=1			,      )
  return checkpoint_callback
def 				lowerCamelCase__	(						_A			,      _A    ):
  return EarlyStopping(
      monitor=f"""val_{metric}"""			,      mode='min' if 'loss' in metric else 'max'			,      patience=_A			,      verbose=_A			,      )

class 							a__( pl.Callback     ):







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Any						,      __snake_case			:  Optional[int]     ):
      a						: str					       =					{F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
      pl_module.logger.log_metrics(__snake_case     )







    @rank_zero_only
    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  pl.LightningModule						,      __snake_case			:  str						,      __snake_case			:  List[str]=True     ):
      logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
      a						: Dict					       =					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						: List[str]					       =					Path(pl_module.hparams.output_dir     )
      if type_path == "test":
        a						: str					       =					od / 'test_results.txt'
        a						: 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						: List[Any]					       =					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
        a						: List[Any]					       =					od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
        results_file.parent.mkdir(exist_ok=__snake_case     )
        generations_file.parent.mkdir(exist_ok=__snake_case     )
      with open(__snake_case						,      'a+'     ) as writer:
        for key in sorted(__snake_case     ):
          if key in ["log", "progress_bar", "preds"]:
            continue
          a						: Tuple					       =					metrics[key]
          if isinstance(__snake_case						,      torch.Tensor     ):
            a						: List[str]					       =					val.item()
          a						: Dict					       =					F"""{key}: {val:.6f}\n"""
          writer.write(__snake_case     )

      if not save_generations:
        return

      if "preds" in metrics:
        a						: List[Any]					       =					'\n'.join(metrics['preds']     )
        generations_file.open('w+'     ).write(__snake_case     )







    @rank_zero_only
    def       lowercase_	(				self			:  str						,      __snake_case			:  str						,      __snake_case			:  int     ):
      try:
        a						: str					       =					pl_module.model.model.num_parameters()
      except AttributeError:
        a						: Optional[Any]					       =					pl_module.model.num_parameters()

      a						: int					       =					count_trainable_parameters(__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       lowercase_	(				self			:  List[str]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  pl.LightningModule     ):
      save_json(pl_module.metrics						,      pl_module.metrics_save_path     )
      return self._write_logs(__snake_case						,      __snake_case						,      'test'     )







    @rank_zero_only
    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  pl.Trainer						,      __snake_case			:  Dict     ):
      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 torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends