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[int]:







      '''simple docstring'''


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


UpperCamelCase__											=					logging.getLogger(__name__)




def    lowerCAmelCase_ (     __A,  __A							)     ->				str:







      '''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,  __A							)     ->				List[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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[Any]		)						->				List[str]:
         """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(__UpperCAmelCase		)


   @rank_zero_only
   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						pl.Trainer       ,  __UpperCAmelCase     :						pl.LightningModule       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						List[Any]=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=__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
                     UpperCAmelCase__								=  metrics[key]
                     if isinstance(__UpperCAmelCase       ,  torch.Tensor		):
                           UpperCAmelCase__								=  val.item()
                     UpperCAmelCase__								=  f"""{key}: {val:.6f}\n"""
                     writer.write(__UpperCAmelCase		)

         if not save_generations:
               return

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


   @rank_zero_only
   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Optional[int]:
         """simple docstring"""



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

         UpperCAmelCase__								=  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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						pl.Trainer       ,  __UpperCAmelCase     :						pl.LightningModule		)						->				Union[str, Any]:
         """simple docstring"""



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





   @rank_zero_only
   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						pl.Trainer       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				int:
         """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")




"""simple docstring"""






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		lowerCAmelCase__     :str = None					,		) ->    Optional[int]:
      '''simple docstring'''


      if config_name_or_path is None:
            lowercase														=	"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

      if generator_tokenizer_name_or_path is None:
            lowercase														=	generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            lowercase														=	question_encoder_name_or_path

      lowercase														=	RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

      # Save model.
      lowercase														=	RagConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)
      lowercase														=	AutoConfig.from_pretrained(lowerCAmelCase__		)

      lowercase														=	gen_config
      lowercase														=	question_encoder_config

      lowercase														=	model_class.from_pretrained_question_encoder_generator(
          lowerCAmelCase__					,		lowerCAmelCase__					,		config=lowerCAmelCase__		)
      rag_model.save_pretrained(lowerCAmelCase__		)

      # Sanity check.
      model_class.from_pretrained(lowerCAmelCase__		)

      # Save tokenizers.
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""		)
      lowercase														=	AutoTokenizer.from_pretrained(lowerCAmelCase__		)
      question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""		)


if __name__ == "__main__":
       __lowerCAmelCase    :       int					     =argparse.ArgumentParser()
       parser.add_argument(
           """--model_type""",
           choices=["""rag_sequence""", """rag_token"""],
           required=True,
           type=str,
           help="""RAG model type: rag_sequence, rag_token""",
       )
       parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
       parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
       parser.add_argument(
           """--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
       )

       parser.add_argument(
           """--generator_tokenizer_name_or_path""",
           type=str,
           help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
       )
       parser.add_argument(
           """--question_encoder_tokenizer_name_or_path""",
           type=str,
           help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
       )
       parser.add_argument(
           """--config_name_or_path""",
           type=str,
           help=(
               """Identifier of the model config to use, if not provided, resolves to a base config for a given"""
               """ ``model_type``"""
           ),
       )

       __lowerCAmelCase    :       List[str]					     =parser.parse_args()

       __lowerCAmelCase    :       Dict					     =Path(args.dest)
       dest_dir.mkdir(exist_ok=True)

       consolidate(
           args.model_type,
           args.generator_name_or_path,
           args.question_encoder_name_or_path,
           dest_dir,
           args.config_name_or_path,
           args.generator_tokenizer_name_or_path,
           args.question_encoder_tokenizer_name_or_path,
       )



