





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


_snake_case  :    Any						    =  logging.getLogger(__name__)

def   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			List[str]	):
   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=UpperCAmelCase_,	filename=UpperCAmelCase_,	monitor=F"""val_{metric}""",	mode='max',	save_top_k=3,	every_n_epochs=1,	)
   return checkpoint_callback

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




class 				_UpperCAmelCase    (    pl.Callback    ):

  """simple docstring"""


  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		str     )							->						str:
     __lowerCAmelCase								=	{f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
     pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_     )






  @rank_zero_only
  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		List[str]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Tuple=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=SCREAMING_SNAKE_CASE_     )
        generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
     with open(SCREAMING_SNAKE_CASE_					,  'a+'     ) as writer:
        for key in sorted(SCREAMING_SNAKE_CASE_     ):
           if key in ["log", "progress_bar", "preds"]:
              continue
           __lowerCAmelCase								=	metrics[key]
           if isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor     ):
              __lowerCAmelCase								=	val.item()
           __lowerCAmelCase								=	f"""{key}: {val:.6f}\n"""
           writer.write(SCREAMING_SNAKE_CASE_     )

     if not save_generations:
        return

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






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

     __lowerCAmelCase								=	count_trainable_parameters(SCREAMING_SNAKE_CASE_     )
     # 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 :		Dict					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Optional[Any]     )							->						Optional[Any]:
     save_json(pl_module.metrics					,  pl_module.metrics_save_path     )
     return self._write_logs(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  'test'     )






  @rank_zero_only
  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		Any     )							->						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_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






