




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       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  Union[str, Any]:
     a											=   filter(lambda __UpperCamelCase: p.requires_grad	,      model.parameters())
     a											=   sum([np.prod(p.size()) for p in model_parameters])
     return params


lowercase__   :    Tuple 					=      logging.getLogger(__name__)




class     a__ (      pl.Callback   ):







     def     lowerCAmelCase_       (			self						,	A						,	A				)						->					Union[str, Any]:



          '''simple docstring'''



          a											=   {F'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups				)}
          pl_module.logger.log_metrics(A				)







     @rank_zero_only
     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A=True				)						->					None:



          '''simple docstring'''



          logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
          a											=   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											=   Path(pl_module.hparams.output_dir				)
          if type_path == "test":
               a											=   od / "test_results.txt"
               a											=   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											=   od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               a											=   od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
               results_file.parent.mkdir(exist_ok=A				)
               generations_file.parent.mkdir(exist_ok=A				)
          with open(A						,	"a+"				) as writer:
               for key in sorted(A				):
                    if key in ["log", "progress_bar", "preds"]:
                         continue
                    a											=   metrics[key]
                    if isinstance(A						,	torch.Tensor				):
                         a											=   val.item()
                    a											=   F'''{key}: {val:.6f}\n'''
                    writer.write(A				)

          if not save_generations:
               return

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







     @rank_zero_only
     def     lowerCAmelCase_       (			self						,	A						,	A				)						->					List[Any]:



          '''simple docstring'''



          try:
               a											=   pl_module.model.model.num_parameters()
          except AttributeError:
               a											=   pl_module.model.num_parameters()

          a											=   count_trainable_parameters(A				)
          # 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						,	A						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          save_json(pl_module.metrics						,	pl_module.metrics_save_path				)
          return self._write_logs(A						,	A						,	"test"				)







     @rank_zero_only
     def     lowerCAmelCase_       (			self						,	A						,	A				)						->					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")







def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase=1	,      __UpperCamelCase=False)    ->  Optional[int]:
     if metric == "rouge2":
          a											=   "{val_avg_rouge2:.4f}-{step_count}"
     elif metric == "bleu":
          a											=   "{val_avg_bleu:.4f}-{step_count}"
     elif metric == "loss":
          a											=   "{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											=   ModelCheckpoint(
         dirpath=__UpperCamelCase	,      filename=__UpperCamelCase	,      monitor=f'''val_{metric}'''	,      mode="min" if "loss" in metric else "max"	,      save_top_k=__UpperCamelCase	,      )
     return checkpoint_callback





def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  int:
     return EarlyStopping(
         monitor=f'''val_{metric}'''	,      mode="min" if "loss" in metric else "max"	,      patience=__UpperCamelCase	,      verbose=__UpperCamelCase	,      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__   :    Union[str, Any] 					=      {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__   :    List[Any] 					=      [
       "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "VisualBertForMultipleChoice",
       "VisualBertForPreTraining",
       "VisualBertForQuestionAnswering",
       "VisualBertForRegionToPhraseAlignment",
       "VisualBertForVisualReasoning",
       "VisualBertLayer",
       "VisualBertModel",
       "VisualBertPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_visual_bert import (
          VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VisualBertForMultipleChoice,
          VisualBertForPreTraining,
          VisualBertForQuestionAnswering,
          VisualBertForRegionToPhraseAlignment,
          VisualBertForVisualReasoning,
          VisualBertLayer,
          VisualBertModel,
          VisualBertPreTrainedModel,
      )


else:
   import sys

   lowercase__   :    Union[str, Any] 					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

