





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case  :    Union[str, Any]						    =  logging.get_logger(__name__)

_snake_case  :    Union[str, Any]						    =  {
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}



class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """focalnet"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Optional[int]=2_2_4					,  lowerCAmelCase_ :		int=4					,  lowerCAmelCase_ :		Optional[int]=3					,  lowerCAmelCase_ :		Tuple=9_6					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		List[str]=[1_9_2, 3_8_4, 7_6_8, 7_6_8]					,  lowerCAmelCase_ :		Any=[2, 2, 6, 2]					,  lowerCAmelCase_ :		Dict=[2, 2, 2, 2]					,  lowerCAmelCase_ :		Union[str, Any]=[3, 3, 3, 3]					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		List[Any]=4.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Dict=1e-4					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		Optional[int]=False					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Union[str, Any]=1e-5					,  lowerCAmelCase_ :		Union[str, Any]=3_2					,  lowerCAmelCase_ :		Union[str, Any]=None					,  lowerCAmelCase_ :		Dict=None					,  **lowerCAmelCase_ :		List[Any]					,  )							->						List[Any]:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	embed_dim
     __lowerCAmelCase								=	use_conv_embed
     __lowerCAmelCase								=	hidden_sizes
     __lowerCAmelCase								=	depths
     __lowerCAmelCase								=	focal_levels
     __lowerCAmelCase								=	focal_windows
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	mlp_ratio
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_layerscale
     __lowerCAmelCase								=	layerscale_value
     __lowerCAmelCase								=	use_post_layernorm
     __lowerCAmelCase								=	use_post_layernorm_in_modulation
     __lowerCAmelCase								=	normalize_modulator
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps
     __lowerCAmelCase								=	encoder_stride
     __lowerCAmelCase								=	['stem'] + [f"""stage{idx}""" for idx in range(1					,  len(self.depths     ) + 1     )]
     __lowerCAmelCase	,	__lowerCAmelCase								=	get_aligned_output_features_output_indices(
         out_features=lowerCAmelCase_					,  out_indices=lowerCAmelCase_					,  stage_names=self.stage_names     )








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   a_    ( lowerCAmelCase_      :			Optional[int]	):
   __lowerCAmelCase								=	filter(lambda lowerCAmelCase_	: p.requires_grad,	model.parameters()	)
   __lowerCAmelCase								=	sum([np.prod(p.size()	) for p in model_parameters]	)
   return params


_snake_case  :    Dict						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[int]	):
   if metric == "rouge2":
      __lowerCAmelCase								=	'{val_avg_rouge2:.4f}-{step_count}'
   elif metric == "bleu":
      __lowerCAmelCase								=	'{val_avg_bleu:.4f}-{step_count}'
   elif metric == "em":
      __lowerCAmelCase								=	'{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.'	)

   __lowerCAmelCase								=	ModelCheckpoint(
       dirpath=lowerCAmelCase_,	filename=lowerCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

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




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int     )							->						Any:
     __lowerCAmelCase								=	{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 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[Any]=True     )							->						None:
     logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""     )
     __lowerCAmelCase								=	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
     __lowerCAmelCase								=	Path(pl_module.hparams.output_dir     )
     if type_path == "test":
        __lowerCAmelCase								=	od / 'test_results.txt'
        __lowerCAmelCase								=	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.
        __lowerCAmelCase								=	od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
        __lowerCAmelCase								=	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
           __lowerCAmelCase								=	metrics[key]
           if isinstance(lowerCAmelCase_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(lowerCAmelCase_     )

     if not save_generations:
        return

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






  @rank_zero_only
  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Dict:
     try:
        __lowerCAmelCase								=	pl_module.model.model.num_parameters()
     except AttributeError:
        __lowerCAmelCase								=	pl_module.model.num_parameters()

     __lowerCAmelCase								=	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 				lowercase		(				self :		int					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		pl.LightningModule     )							->						Any:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(lowerCAmelCase_					,  lowerCAmelCase_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		pl.Trainer					,  lowerCAmelCase_ :		Any     )							->						int:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")

