


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_					(  snake_case					)  ->    List[Any]:
       lowercase__:       Optional[int]        =       filter(lambda snake_case					: p.requires_grad      ,							model.parameters()					)
       lowercase__:       Any        =       sum([np.prod(p.size()					) for p in model_parameters]					)
       return params


__lowerCAmelCase			=							logging.getLogger(__name__)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Union[str, Any]:
       if metric == "rouge2":
              lowercase__:       int        =       '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              lowercase__:       Union[str, Any]        =       '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              lowercase__:       int        =       '{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.'					)

       lowercase__:       List[Any]        =       ModelCheckpoint(
           dirpath=snake_case      ,							filename=snake_case      ,							monitor=f'val_{metric}'      ,							mode='max'      ,							save_top_k=3      ,							every_n_epochs=1      ,							)
       return checkpoint_callback




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Dict:
       return EarlyStopping(
           monitor=f'val_{metric}'      ,							mode='min' if 'loss' in metric else 'max'      ,							patience=snake_case      ,							verbose=snake_case      ,							)





class   __a      (   pl.Callback				):
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    str:




        '''simple docstring'''
        lowercase__:       Tuple        =       {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    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=True			) ->    None:




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

        if not save_generations:
               return

        if "preds" in metrics:
               lowercase__:       Tuple        =       '\n'.join(metrics['preds']			)
               generations_file.open('w+'			).write(lowerCAmelCase__			)
 @rank_zero_only
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        try:
               lowercase__:       int        =       pl_module.model.model.num_parameters()
        except AttributeError:
               lowercase__:       List[str]        =       pl_module.model.num_parameters()

        lowercase__:       List[Any]        =       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    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Any:




        '''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    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Optional[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")







from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer





 class   __a      (   __UpperCamelCase				):
  def __init__(       self    ,			lowerCAmelCase__ = 101			) ->    Any:




         '''simple docstring'''
         lowercase__:       Any        =       length
  def __len__(       self			) ->    List[Any]:




         '''simple docstring'''
         return self.length
  def __getitem__(       self    ,			lowerCAmelCase__			) ->    int:




         '''simple docstring'''
         return i





 class   __a      :
  def __call__(       self    ,			lowerCAmelCase__			) ->    List[str]:




         '''simple docstring'''
         return {"input_ids": torch.tensor(lowerCAmelCase__			), "labels": torch.tensor(lowerCAmelCase__			)}





 class   __a      (   nn.Module				):
  def __init__(       self			) ->    Tuple:




         '''simple docstring'''
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         lowercase__:       List[str]        =       nn.Linear(120    ,			80			)
  def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=None			) ->    int:




         '''simple docstring'''
         if labels is not None:
                return torch.tensor(0.0    ,			device=input_ids.device			), input_ids
         else:
                return input_ids





class   __a      (   __UpperCamelCase				):
 @require_torch_neuroncore
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       int        =       F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       Optional[int]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call





class   __a      (   __UpperCamelCase				):
 @require_torch_multi_gpu
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       List[str]        =       F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
        lowercase__:       Tuple        =       self.get_auto_remove_tmp_dir()
        lowercase__:       List[str]        =       F'--output_dir {output_dir}'.split()
        lowercase__:       int        =       ['torchrun'] + distributed_args + args
        execute_subprocess_async(lowerCAmelCase__    ,			env=self.get_env()			)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 __lowerCAmelCase			=							HfArgumentParser((TrainingArguments,))
 __lowerCAmelCase			=							parser.parse_args_into_dataclasses()[0]

 logger.warning(
     F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
     F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [1_01, 40, 7]:
  __lowerCAmelCase			=							DummyDataset(dataset_length)




  def        snake_case_					(  snake_case					)  ->    Dict:
         lowercase__:       str        =       list(range(len(snake_case					)					)					)
         lowercase__:       Tuple        =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         if not success and training_args.local_rank == 0:
                logger.warning(
                    'Predictions and/or labels do not match expected results:\n  - predictions: '
                    f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'					)
         return {"success": success}

  __lowerCAmelCase			=							Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  __lowerCAmelCase			=							2

  __lowerCAmelCase			=							trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  __lowerCAmelCase			=							trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)




  __lowerCAmelCase			=							None


