


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__			(      a__:		Optional[Any]	)  ->		Any:


       '''simple docstring'''





       _UpperCAmelCase					=      filter(lambda a__	: p.requires_grad      ,  model.parameters()	)
       _UpperCAmelCase					=      sum([np.prod(p.size()	) for p in model_parameters]	)
       return params


lowerCAmelCase__  :Any             =							logging.getLogger(__name__)

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		List[str]	)  ->		Union[str, Any]:


       '''simple docstring'''





       if metric == "rouge2":
              _UpperCAmelCase					=      '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              _UpperCAmelCase					=      '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              _UpperCAmelCase					=      '{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.'	)

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

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Dict	)  ->		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      ):
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




         """simple docstring"""





         _UpperCAmelCase					=      {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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=True						)			->	None:




         """simple docstring"""





         logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''						)
         _UpperCAmelCase					=      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
         _UpperCAmelCase					=      Path(pl_module.hparams.output_dir						)
         if type_path == "test":
                _UpperCAmelCase					=      od / 'test_results.txt'
                _UpperCAmelCase					=      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.
                _UpperCAmelCase					=      od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
                _UpperCAmelCase					=      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
                       _UpperCAmelCase					=      metrics[key]
                       if isinstance(_SCREAMING_SNAKE_CASE      ,   torch.Tensor						):
                              _UpperCAmelCase					=      val.item()
                       _UpperCAmelCase					=      f'''{key}: {val:.6f}\n'''
                       writer.write(_SCREAMING_SNAKE_CASE						)

         if not save_generations:
                return

         if "preds" in metrics:
                _UpperCAmelCase					=      '\n'.join(metrics['preds']						)
                generations_file.open('w+'						).write(_SCREAMING_SNAKE_CASE						)
  @rank_zero_only
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




         """simple docstring"""





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

         _UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




         """simple docstring"""





         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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




         """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")




from typing import TYPE_CHECKING

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


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

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


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''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

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
