


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_						( __lowercase       ):
   UpperCAmelCase_							:      Any						=   filter(lambda __lowercase       : p.requires_grad			,       model.parameters()       )
   UpperCAmelCase_							:      Optional[Any]						=   sum([np.prod(p.size()       ) for p in model_parameters]       )
   return params


__UpperCamelCase				:  Dict      					=							logging.getLogger(__name__)
def      snake_case_						( __lowercase			,       __lowercase       ):
   if metric == "rouge2":
      UpperCAmelCase_							:      List[Any]						=   '''{val_avg_rouge2:.4f}-{step_count}'''
   elif metric == "bleu":
      UpperCAmelCase_							:      str						=   '''{val_avg_bleu:.4f}-{step_count}'''
   elif metric == "em":
      UpperCAmelCase_							:      Optional[Any]						=   '''{val_avg_em:.4f}-{step_count}'''
   elif metric == "loss":
      UpperCAmelCase_							:      Union[str, Any]						=   '''{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.'''       )

   UpperCAmelCase_							:      Optional[int]						=   ModelCheckpoint(
       dirpath=__lowercase			,       filename=__lowercase			,       monitor=F'''val_{metric}'''			,       mode='''max'''			,       save_top_k=1			,       every_n_epochs=1			,       )

   return checkpoint_callback
def      snake_case_						( __lowercase			,       __lowercase       ):
   return EarlyStopping(
       monitor=F'''val_{metric}'''			,       mode='''min''' if '''loss''' in metric else '''max'''			,       patience=__lowercase			,       verbose=__lowercase			,       )





class  lowerCAmelCase__(    pl.Callback						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Dict       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   {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 					_lowerCamelCase (	self				:   Tuple       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   pl.LightningModule       ,   __snake_case				:   str       ,   __snake_case				:   Optional[Any]=True	):





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

       if not save_generations:
          return

       if "preds" in metrics:
          UpperCAmelCase_							:      Union[str, Any]						=   '''\n'''.join(metrics['''preds''']	)
          generations_file.open('''w+'''	).write(__snake_case	)





    @rank_zero_only
    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       try:
          UpperCAmelCase_							:      List[str]						=   pl_module.model.model.num_parameters()
       except AttributeError:
          UpperCAmelCase_							:      Union[str, Any]						=   pl_module.model.num_parameters()

       UpperCAmelCase_							:      Tuple						=   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 					_lowerCamelCase (	self				:   List[str]       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   pl.LightningModule	):





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





    @rank_zero_only
    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   pl.Trainer       ,   __snake_case				:   Optional[int]	):





       '''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")



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase				:  str      					=							'true'
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=1_6       ):
   set_seed(4_2       )
   UpperCAmelCase_							:      Optional[int]						=   RegressionModel()
   UpperCAmelCase_							:      Optional[int]						=   deepcopy(__lowercase       )
   UpperCAmelCase_							:      Union[str, Any]						=   RegressionDataset(length=__lowercase       )
   UpperCAmelCase_							:      Any						=   DataLoader(__lowercase			,       batch_size=__lowercase       )
   model.to(accelerator.device       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return model, ddp_model, dataloader
def      snake_case_						( __lowercase			,       __lowercase=False       ):
   UpperCAmelCase_							:      Optional[int]						=   AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''       )
   UpperCAmelCase_							:      List[Any]						=   load_dataset('''glue'''			,       '''mrpc'''			,       split='''validation'''       )

   def tokenize_function(__lowercase       ):
      UpperCAmelCase_							:      int						=   tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=__lowercase			,       max_length=__lowercase       )
      return outputs

   with accelerator.main_process_first():
      UpperCAmelCase_							:      List[str]						=   dataset.map(
          __lowercase			,       batched=__lowercase			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       )

   UpperCAmelCase_							:      Any						=   tokenized_datasets.rename_column('''label'''			,       '''labels'''       )

   def collate_fn(__lowercase       ):
      if use_longest:
         return tokenizer.pad(__lowercase			,       padding='''longest'''			,       return_tensors='''pt'''       )
      return tokenizer.pad(__lowercase			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''       )

   return DataLoader(__lowercase			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=1_6       )
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[int]						=   Accelerator(dispatch_batches=__lowercase			,       split_batches=__lowercase       )
   UpperCAmelCase_							:      int						=   get_dataloader(__lowercase			,       not dispatch_batches       )
   UpperCAmelCase_							:      Optional[int]						=   AutoModelForSequenceClassification.from_pretrained(
       '''hf-internal-testing/mrpc-bert-base-cased'''			,       return_dict=__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   accelerator.prepare(__lowercase			,       __lowercase       )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Dict						=   []
   for batch in dataloader:
      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   batch.values()
      with torch.no_grad():
         UpperCAmelCase_							:      List[Any]						=   model(__lowercase       )
         UpperCAmelCase_		,      UpperCAmelCase_							:      Dict						=   accelerator.gather_for_metrics((logit, target)       )
         logits_and_targets.append((logit, target)       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   [], []
   for logit, targ in logits_and_targets:
      logits.append(__lowercase       )
      targs.append(__lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   torch.cat(__lowercase       ), torch.cat(__lowercase       )
   return logits, targs
def      snake_case_						( __lowercase			,       __lowercase=8_2			,       __lowercase=False			,       __lowercase=False			,       __lowercase=1_6       ):
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Any						=   get_basic_setup(__lowercase			,       __lowercase			,       __lowercase       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   generate_predictions(__lowercase			,       __lowercase			,       __lowercase       )
   assert (
       len(__lowercase       ) == num_samples
   ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowercase       )}'''
def      snake_case_						( __lowercase = False			,       __lowercase = False       ):
   UpperCAmelCase_							:      Optional[Any]						=   evaluate.load('''glue'''			,       '''mrpc'''       )
   UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   get_mrpc_setup(__lowercase			,       __lowercase       )
   # First do baseline
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Union[str, Any]						=   setup['''no''']
   model.to(__lowercase       )
   model.eval()
   for batch in dataloader:
      batch.to(__lowercase       )
      with torch.inference_mode():
         UpperCAmelCase_							:      str						=   model(**__lowercase       )
      UpperCAmelCase_							:      Dict						=   outputs.logits.argmax(dim=-1       )
      metric.add_batch(predictions=__lowercase			,       references=batch['''labels''']       )
   UpperCAmelCase_							:      Optional[int]						=   metric.compute()

   # Then do distributed
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         UpperCAmelCase_							:      Optional[int]						=   model(**__lowercase       )
      UpperCAmelCase_							:      int						=   outputs.logits.argmax(dim=-1       )
      UpperCAmelCase_							:      Optional[int]						=   batch['''labels''']
      UpperCAmelCase_		,      UpperCAmelCase_							:      Tuple						=   accelerator.gather_for_metrics((preds, references)       )
      metric.add_batch(predictions=__lowercase			,       references=__lowercase       )
   UpperCAmelCase_							:      Dict						=   metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]			,       distributed[key]       ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def      snake_case_						( ):
   UpperCAmelCase_							:      str						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print('''**Testing gather_for_metrics**'''       )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''       )
            test_mrpc(__lowercase			,       __lowercase       )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test torch metrics**'''       )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         UpperCAmelCase_							:      Optional[Any]						=   Accelerator(split_batches=__lowercase			,       dispatch_batches=__lowercase       )
         if accelerator.is_local_main_process:
            print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''       )
         test_torch_metrics(__lowercase			,       9_9       )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print('''**Test last batch is not dropped when perfectly divisible**'''       )
   UpperCAmelCase_							:      List[Any]						=   Accelerator()
   test_torch_metrics(__lowercase			,       5_1_2       )
   accelerator.state._reset_state()
def      snake_case_						( __lowercase       ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
       main()