



"""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    (		__UpperCamelCase   :      str     ):


						'''simple docstring'''






						snake_case_ :			List[str]            =      filter(lambda __UpperCamelCase     : p.requires_grad       ,			model.parameters()     )
						snake_case_ :			List[str]            =      sum([np.prod(p.size()     ) for p in model_parameters]     )
						return params


__lowerCAmelCase							:       Tuple          =       logging.getLogger(__name__)
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Optional[Any]       ,			__UpperCamelCase   :      Optional[Any]     ):


						'''simple docstring'''






						if metric == "rouge2":
												snake_case_ :			List[str]            =      """{val_avg_rouge2:.4f}-{step_count}"""
						elif metric == "bleu":
												snake_case_ :			Optional[int]            =      """{val_avg_bleu:.4f}-{step_count}"""
						elif metric == "em":
												snake_case_ :			Union[str, Any]            =      """{val_avg_em:.4f}-{step_count}"""
						elif metric == "loss":
												snake_case_ :			int            =      """{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."""     )

						snake_case_ :			Union[str, Any]            =      ModelCheckpoint(
						    dirpath=__UpperCamelCase       ,			filename=__UpperCamelCase       ,			monitor=F'val_{metric}'       ,			mode="""max"""       ,			save_top_k=1       ,			every_n_epochs=1       ,			)

						return checkpoint_callback
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple       ,			__UpperCamelCase   :      int     ):


						'''simple docstring'''






						return EarlyStopping(
						    monitor=F'val_{metric}'       ,			mode="""min""" if """loss""" in metric else """max"""       ,			patience=__UpperCamelCase       ,			verbose=__UpperCamelCase       ,			)






class  _lowerCAmelCase     (					pl.Callback		):





		"""simple docstring"""






		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			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(_lowercase )





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase=True )			->    None:







								'''simple docstring'''

								logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
								snake_case_ :			List[str]            =      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
								snake_case_ :			List[str]            =      Path(pl_module.hparams.output_dir )
								if type_path == "test":
														snake_case_ :			Dict            =      od / """test_results.txt"""
														snake_case_ :			Dict            =      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.
														snake_case_ :			str            =      od / f'{type_path}_results/{trainer.global_step:05d}.txt'
														snake_case_ :			Tuple            =      od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
														results_file.parent.mkdir(exist_ok=_lowercase )
														generations_file.parent.mkdir(exist_ok=_lowercase )
								with open(_lowercase				,     """a+""" ) as writer:
														for key in sorted(_lowercase ):
																				if key in ["log", "progress_bar", "preds"]:
																										continue
																				snake_case_ :			int            =      metrics[key]
																				if isinstance(_lowercase				,     torch.Tensor ):
																										snake_case_ :			Optional[Any]            =      val.item()
																				snake_case_ :			int            =      f'{key}: {val:.6f}\n'
																				writer.write(_lowercase )

								if not save_generations:
														return

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





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    Optional[Any]:







								'''simple docstring'''

								try:
														snake_case_ :			str            =      pl_module.model.model.num_parameters()
								except AttributeError:
														snake_case_ :			List[str]            =      pl_module.model.num_parameters()

								snake_case_ :			str            =      count_trainable_parameters(_lowercase )
								# 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				,     _lowercase				,     _lowercase )			->    List[str]:







								'''simple docstring'''

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





		@rank_zero_only
		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase )			->    Any:







								'''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 argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands

def 	_lowerCAmelCase (    ):
      lowercase__  =	ArgumentParser('Transformers CLI tool'    ,	usage='transformers-cli <command> [<args>]'						)
      lowercase__  =	parser.add_subparsers(help='transformers-cli command helpers'						)

      # Register commands
      ConvertCommand.register_subcommand(A__						)
      DownloadCommand.register_subcommand(A__						)
      EnvironmentCommand.register_subcommand(A__						)
      RunCommand.register_subcommand(A__						)
      ServeCommand.register_subcommand(A__						)
      UserCommands.register_subcommand(A__						)
      AddNewModelCommand.register_subcommand(A__						)
      AddNewModelLikeCommand.register_subcommand(A__						)
      LfsCommands.register_subcommand(A__						)
      PTtoTFCommand.register_subcommand(A__						)

      # Let's go
      lowercase__  =	parser.parse_args()

      if not hasattr(A__    ,	'func'						):
            parser.print_help()
            exit(1						)

      # Run
      lowercase__  =	args.func(A__						)
      service.run()


if __name__ == "__main__":
       main()
