

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       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


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


UpperCamelCase__  :      Tuple			=    logging.getLogger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


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

    SCREAMING_SNAKE_CASE_									=						ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE ,   filename=_SCREAMING_SNAKE_CASE ,   monitor=f"""val_{metric}""" ,   mode='max' ,   save_top_k=3 ,   every_n_epochs=1 ,   )
    return checkpoint_callback


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


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





class       __snake_case  (    pl.Callback       ):

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_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 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A=True):
      logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""")
      SCREAMING_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
      SCREAMING_SNAKE_CASE_									=						Path(pl_module.hparams.output_dir)
      if type_path == "test":
          SCREAMING_SNAKE_CASE_									=						od / 'test_results.txt'
          SCREAMING_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.
          SCREAMING_SNAKE_CASE_									=						od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
          SCREAMING_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
              SCREAMING_SNAKE_CASE_									=						metrics[key]
              if isinstance(_A						,		torch.Tensor):
                  SCREAMING_SNAKE_CASE_									=						val.item()
              SCREAMING_SNAKE_CASE_									=						f"""{key}: {val:.6f}\n"""
              writer.write(_A)

      if not save_generations:
          return

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

  @rank_zero_only
  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      try:
          SCREAMING_SNAKE_CASE_									=						pl_module.model.model.num_parameters()
      except AttributeError:
          SCREAMING_SNAKE_CASE_									=						pl_module.model.num_parameters()

      SCREAMING_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 						lowerCAmelCase__     (					self						,		_A						,		_A):
      save_json(pl_module.metrics						,		pl_module.metrics_save_path)
      return self._write_logs(_A						,		_A						,		'test')


  @rank_zero_only
  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      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

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


