


"""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_rag import save_json


def 					lowercase      (    A_	)->				int:
  '''simple docstring'''







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


__lowercase    =				logging.getLogger(__name__)


def 					lowercase      (    A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  if metric == "rouge2":
    a						:		List[str]											=      "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    a						:		List[Any]											=      "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "em":
    a						:		List[Any]											=      "{val_avg_em:.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."	)

  a						:		Dict											=      ModelCheckpoint(
      dirpath=A_  ,       filename=A_  ,       monitor=F'''val_{metric}'''  ,       mode="max"  ,       save_top_k=3  ,       every_n_epochs=1  ,       )
  return checkpoint_callback


def 					lowercase      (    A_  ,       A_	)->				Union[str, Any]:
  '''simple docstring'''







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







class 						_A  (  pl.Callback  ):







    """simple docstring"""




    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   str):
      a						:		Any											=      {f'''lr_group_{i}''': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
      pl_module.logger.log_metrics(__UpperCAmelCase)






    @rank_zero_only
    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   pl.Trainer    ,			__UpperCAmelCase    :   pl.LightningModule    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   List[str]=True):
      logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''')
      a						:		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						:		Optional[Any]											=      od / "test_results.txt"
        a						:		Union[str, 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						:		Tuple											=      od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
        a						:		List[Any]											=      od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
        results_file.parent.mkdir(exist_ok=__UpperCAmelCase)
        generations_file.parent.mkdir(exist_ok=__UpperCAmelCase)
      with open(__UpperCAmelCase    ,			"a+") as writer:
        for key in sorted(__UpperCAmelCase):
          if key in ["log", "progress_bar", "preds"]:
            continue
          a						:		Union[str, Any]											=      metrics[key]
          if isinstance(__UpperCAmelCase    ,			torch.Tensor):
            a						:		List[Any]											=      val.item()
          a						:		Any											=      f'''{key}: {val:.6f}\n'''
          writer.write(__UpperCAmelCase)

      if not save_generations:
        return

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






    @rank_zero_only
    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   List[Any]    ,			__UpperCAmelCase    :   List[Any]):
      try:
        a						:		int											=      pl_module.model.model.num_parameters()
      except AttributeError:
        a						:		int											=      pl_module.model.num_parameters()

      a						:		Union[str, Any]											=      count_trainable_parameters(__UpperCAmelCase)
      # 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    :   Union[str, Any]    ,			__UpperCAmelCase    :   pl.Trainer    ,			__UpperCAmelCase    :   pl.LightningModule):
      save_json(pl_module.metrics    ,			pl_module.metrics_save_path)
      return self._write_logs(__UpperCAmelCase    ,			__UpperCAmelCase    ,			"test")






    @rank_zero_only
    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   pl.Trainer    ,			__UpperCAmelCase    :   str):
      save_json(pl_module.metrics    ,			pl_module.metrics_save_path)
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






