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_							(     _lowerCamelCase				:			Union[str, Any]):
    lowercase__					:  Dict          =							filter(lambda _lowerCamelCase: p.requires_grad						,      model.parameters())
    lowercase__					:  Dict          =							sum([np.prod(p.size()) for p in model_parameters])
    return params


UpperCamelCase    				=		logging.getLogger(__name__)



class     snake_case_			(						pl.Callback			):






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Union[str, Any]    )   ->		List[str]:
       lowercase__					:  Optional[int]          =							{F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
       pl_module.logger.log_metrics(lowercase_    )






   @rank_zero_only
   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	pl.Trainer      ,		lowercase_							:	pl.LightningModule      ,		lowercase_							:	str      ,		lowercase_							:	Optional[int]=True    )   ->		None:
       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__					:  Tuple          =							Path(pl_module.hparams.output_dir    )
       if type_path == "test":
           lowercase__					:  int          =							od / "test_results.txt"
           lowercase__					:  Dict          =							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__					:  Dict          =							od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
           lowercase__					:  int          =							od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
           results_file.parent.mkdir(exist_ok=lowercase_    )
           generations_file.parent.mkdir(exist_ok=lowercase_    )
       with open(lowercase_      ,		"a+"    ) as writer:
           for key in sorted(lowercase_    ):
               if key in ["log", "progress_bar", "preds"]:
                   continue
               lowercase__					:  List[str]          =							metrics[key]
               if isinstance(lowercase_      ,		torch.Tensor    ):
                   lowercase__					:  Optional[Any]          =							val.item()
               lowercase__					:  Tuple          =							F'''{key}: {val:.6f}\n'''
               writer.write(lowercase_    )

       if not save_generations:
           return

       if "preds" in metrics:
           lowercase__					:  Optional[Any]          =							"\n".join(metrics["preds"]    )
           generations_file.open("w+"    ).write(lowercase_    )






   @rank_zero_only
   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Optional[Any]    )   ->		Optional[Any]:
       try:
           lowercase__					:  Dict          =							pl_module.model.model.num_parameters()
       except AttributeError:
           lowercase__					:  Dict          =							pl_module.model.num_parameters()

       lowercase__					:  str          =							count_trainable_parameters(lowercase_    )
       # 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							:	Any      ,		lowercase_							:	pl.Trainer      ,		lowercase_							:	pl.LightningModule    )   ->		str:
       save_json(pl_module.metrics      ,		pl_module.metrics_save_path    )
       return self._write_logs(lowercase_      ,		lowercase_      ,		"test"    )







   @rank_zero_only
   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	pl.Trainer      ,		lowercase_							:	str    )   ->		Tuple:
       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_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Union[str, Any]=1						,      _lowerCamelCase				:			Any=False):
    if metric == "rouge2":
        lowercase__					:  Any          =							"{val_avg_rouge2:.4f}-{step_count}"
    elif metric == "bleu":
        lowercase__					:  Tuple          =							"{val_avg_bleu:.4f}-{step_count}"
    elif metric == "loss":
        lowercase__					:  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.")

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

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			int):
    return EarlyStopping(
        monitor=f'''val_{metric}'''						,      mode="min" if "loss" in metric else "max"						,      patience=_lowerCamelCase						,      verbose=_lowerCamelCase						,      )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



