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 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Dict   ):







    '''simple docstring'''
    __snake_case       :				Any 			=    filter(lambda __SCREAMING_SNAKE_CASE   : p.requires_grad		,							model.parameters()   )
    __snake_case       :				str 			=    sum([np.prod(p.size()   ) for p in model_parameters]   )
    return params


lowercase_  							=			logging.getLogger(__name__)







class      SCREAMING_SNAKE_CASE__					(  pl.Callback    ):

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     Any			):
           __snake_case       :				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 			snake_case__    (							self					:     Tuple			,			_lowerCAmelCase					:     pl.Trainer			,			_lowerCAmelCase					:     pl.LightningModule			,			_lowerCAmelCase					:     str			,			_lowerCAmelCase					:     List[str]=True			):
           logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''			)
           __snake_case       :				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
           __snake_case       :				List[str] 			=    Path(pl_module.hparams.output_dir			)
           if type_path == "test":
               __snake_case       :				Dict 			=    od / """test_results.txt"""
               __snake_case       :				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.
               __snake_case       :				List[Any] 			=    od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
               __snake_case       :				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
                   __snake_case       :				Union[str, Any] 			=    metrics[key]
                   if isinstance(_lowerCAmelCase			,			torch.Tensor			):
                       __snake_case       :				Dict 			=    val.item()
                   __snake_case       :				Dict 			=    f'''{key}: {val:.6f}\n'''
                   writer.write(_lowerCAmelCase			)

           if not save_generations:
               return

           if "preds" in metrics:
               __snake_case       :				int 			=    """\n""".join(metrics["""preds"""]			)
               generations_file.open("""w+"""			).write(_lowerCAmelCase			)

       @rank_zero_only
       def 			snake_case__    (							self					:     Tuple			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Union[str, Any]			):
           try:
               __snake_case       :				List[Any] 			=    pl_module.model.model.num_parameters()
           except AttributeError:
               __snake_case       :				Union[str, Any] 			=    pl_module.model.num_parameters()

           __snake_case       :				Optional[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 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     pl.Trainer			,			_lowerCAmelCase					:     pl.LightningModule			):
           save_json(pl_module.metrics			,			pl_module.metrics_save_path			)
           return self._write_logs(_lowerCAmelCase			,			_lowerCAmelCase			,			"""test"""			)





       @rank_zero_only
       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     pl.Trainer			,			_lowerCAmelCase					:     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")







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					str		,							__SCREAMING_SNAKE_CASE       :					Optional[int]		,							__SCREAMING_SNAKE_CASE       :					Dict=1		,							__SCREAMING_SNAKE_CASE       :					Optional[Any]=False   ):







    '''simple docstring'''
    if metric == "rouge2":
        __snake_case       :				Optional[int] 			=    """{val_avg_rouge2:.4f}-{step_count}"""
    elif metric == "bleu":
        __snake_case       :				List[str] 			=    """{val_avg_bleu:.4f}-{step_count}"""
    elif metric == "loss":
        __snake_case       :				List[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."""   )

    __snake_case       :				Dict 			=    ModelCheckpoint(
        dirpath=__SCREAMING_SNAKE_CASE		,							filename=__SCREAMING_SNAKE_CASE		,							monitor=F'''val_{metric}'''		,							mode="""min""" if """loss""" in metric else """max"""		,							save_top_k=__SCREAMING_SNAKE_CASE		,							)
    return checkpoint_callback







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]		,							__SCREAMING_SNAKE_CASE       :					Tuple   ):







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




import math
from timeit import timeit







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    0
    __snake_case       :				Union[str, Any] 			=    abs(__SCREAMING_SNAKE_CASE   )
    while True:
        __snake_case       :				List[str] 			=    n // 1_0
        digits += 1
        if n == 0:
            break
    return digits







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return 1 if n == 0 else math.floor(math.log(abs(__SCREAMING_SNAKE_CASE   )		,							1_0   ) + 1   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return len(str(abs(__SCREAMING_SNAKE_CASE   )   )   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    from collections.abc import Callable

    def benchmark_a_function(__SCREAMING_SNAKE_CASE       :					Callable		,							__SCREAMING_SNAKE_CASE       :					int   ) -> None:
        __snake_case       :				Tuple 			=    F'''{func.__name__}({value})'''
        __snake_case       :				Union[str, Any] 			=    timeit(F'''__main__.{call}'''		,							setup="""import __main__"""   )
        print(F'''{call}: {func(__SCREAMING_SNAKE_CASE   )} -- {timing} seconds'''   )

    for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



