


'''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      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  Union[str, Any]          =				filter(lambda lowerCamelCase: p.requires_grad							,						model.parameters())
      A_						:  Optional[int]          =				sum([np.prod(p.size()) for p in model_parameters])
      return params


__magic_name__			=      logging.getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[Any]):
      if metric == "rouge2":
            A_						:  Union[str, Any]          =				"""{val_avg_rouge2:.4f}-{step_count}"""
      elif metric == "bleu":
            A_						:  List[Any]          =				"""{val_avg_bleu:.4f}-{step_count}"""
      elif metric == "em":
            A_						:  Optional[int]          =				"""{val_avg_em:.4f}-{step_count}"""
      elif metric == "loss":
            A_						:  Union[str, Any]          =				"""{val_avg_loss:.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_						:  List[str]          =				ModelCheckpoint(
          dirpath=lowerCamelCase							,						filename=lowerCamelCase							,						monitor=F'val_{metric}'							,						mode="""max"""							,						save_top_k=1							,						every_n_epochs=1							,						)

      return checkpoint_callback




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[Any]):
      return EarlyStopping(
          monitor=F'val_{metric}'							,						mode="""min""" if """loss""" in metric else """max"""							,						patience=lowerCamelCase							,						verbose=lowerCamelCase							,						)






class 	__lowerCAmelCase		(							pl.Callback    ):


  '''simple docstring'''







  def        _a							(							self :       str	,_a :       Any	,_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				{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        _a							(							self :       str	,_a :       pl.Trainer	,_a :       pl.LightningModule	,_a :       str	,_a :       str=True ):

        '''simple docstring'''




        logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
        A_						:  int          =				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_						:  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_						:  Dict          =				od / f'{type_path}_results/{trainer.global_step:05d}.txt'
              A_						:  Dict          =				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_						:  Dict          =				metrics[key]
                    if isinstance(_a	,torch.Tensor ):
                          A_						:  Any          =				val.item()
                    A_						:  Optional[int]          =				f'{key}: {val:.6f}\n'
                    writer.write(_a )

        if not save_generations:
              return

        if "preds" in metrics:
              A_						:  List[str]          =				"""\n""".join(metrics["""preds"""] )
              generations_file.open("""w+""" ).write(_a )






  @rank_zero_only
  def        _a							(							self :       str	,_a :       int	,_a :       Optional[int] ):

        '''simple docstring'''




        try:
              A_						:  Union[str, Any]          =				pl_module.model.model.num_parameters()
        except AttributeError:
              A_						:  str          =				pl_module.model.num_parameters()

        A_						:  Dict          =				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        _a							(							self :       Tuple	,_a :       pl.Trainer	,_a :       pl.LightningModule ):

        '''simple docstring'''




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






  @rank_zero_only
  def        _a							(							self :       Any	,_a :       pl.Trainer	,_a :       Dict ):

        '''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")




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__			=      {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['ConvNextFeatureExtractor']
     __magic_name__			=      ['ConvNextImageProcessor']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ConvNextForImageClassification',
         'ConvNextModel',
         'ConvNextPreTrainedModel',
         'ConvNextBackbone',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      [
         'TFConvNextForImageClassification',
         'TFConvNextModel',
         'TFConvNextPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_convnext import ConvNextFeatureExtractor
          from .image_processing_convnext import ConvNextImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnext import (
              CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextBackbone,
              ConvNextForImageClassification,
              ConvNextModel,
              ConvNextPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure)
