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  snake_case  (       snake_case__			:str)						->   int:
       _A        =  filter(lambda snake_case__: p.requires_grad   ,	model.parameters())
       _A        =  sum([np.prod(p.size()) for p in model_parameters])
       return params


_SCREAMING_SNAKE_CASE										=						logging.getLogger(__name__)






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Tuple)						->   Dict:
       if metric == "rouge2":
              _A        =  """{val_avg_rouge2:.4f}-{step_count}"""
       elif metric == "bleu":
              _A        =  """{val_avg_bleu:.4f}-{step_count}"""
       elif metric == "em":
              _A        =  """{val_avg_em:.4f}-{step_count}"""
       elif metric == "loss":
              _A        =  """{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        =  ModelCheckpoint(
           dirpath=_A   ,	filename=_A   ,	monitor=F'''val_{metric}'''   ,	mode="""max"""   ,	save_top_k=1   ,	every_n_epochs=1   ,	)

       return checkpoint_callback






def  snake_case  (       snake_case__			:Tuple   ,	snake_case__			:int)						->   Optional[Any]:
       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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        _A        =  {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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=True							)  ->					Tuple:
        logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''							)
        _A        =  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        =  Path(pl_module.hparams.output_dir							)
        if type_path == "test":
               _A        =  od / """test_results.txt"""
               _A        =  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        =  od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
               _A        =  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        =  metrics[key]
                      if isinstance(UpperCamelCase__		,				torch.Tensor							):
                             _A        =  val.item()
                      _A        =  F'''{key}: {val:.6f}\n'''
                      writer.write(UpperCamelCase__							)

        if not save_generations:
               return

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



 @rank_zero_only
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:



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

        _A        =  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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        save_json(pl_module.metrics		,				pl_module.metrics_save_path							)
        return self._write_logs(UpperCamelCase__		,				UpperCamelCase__		,				"""test"""							)



 @rank_zero_only
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Dict:
        save_json(pl_module.metrics		,				pl_module.metrics_save_path							)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")










import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_A     =			logging.get_logger(__name__)


class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.''',	UpperCamelCase__,	)
								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )




