





"""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 import save_json







def 	__lowercase  (     snake_case_						:						List[str]		)     ->Optional[int]:



    '''simple docstring'''







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


a_				=    logging.getLogger(__name__)






class 	__snake_case      (	pl.Callback  ):





 """simple docstring"""


 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		Union[str, Any]						=  {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       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase=True						):



     '''simple docstring'''




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

     if not save_generations:
         return

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

 @rank_zero_only
 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     try:
         __A					:		Any						=  pl_module.model.model.num_parameters()
     except AttributeError:
         __A					:		List[str]						=  pl_module.model.num_parameters()

     __A					:		Union[str, 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       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     save_json(pl_module.metrics      ,		pl_module.metrics_save_path						)
     return self._write_logs(__lowerCamelCase      ,		__lowerCamelCase      ,		'''test'''						)







 @rank_zero_only
 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''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")







def 	__lowercase  (     snake_case_						:						List[str]       ,snake_case_						:						Union[str, Any]       ,snake_case_						:						Dict=1       ,snake_case_						:						Any=False		)     ->Optional[Any]:



    '''simple docstring'''







    if metric == "rouge2":
        __A					:		List[Any]						=  '''{val_avg_rouge2:.4f}-{step_count}'''
    elif metric == "bleu":
        __A					:		Union[str, Any]						=  '''{val_avg_bleu:.4f}-{step_count}'''
    elif metric == "loss":
        __A					:		Optional[Any]						=  '''{val_avg_loss:.4f}-{step_count}'''
    else:
        raise NotImplementedError(
            F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
            ''' this function.'''		)

    __A					:		List[Any]						=  ModelCheckpoint(
        dirpath=snake_case_       ,filename=snake_case_       ,monitor=F"""val_{metric}"""       ,mode='''min''' if '''loss''' in metric else '''max'''       ,save_top_k=snake_case_       ,)
    return checkpoint_callback







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						List[Any]		)     ->List[str]:



    '''simple docstring'''







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











"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

        for i in range(10 * num_steps		):
            with accelerator.accumulate(snake_case_		):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)




